8 Examples Of computer vision and artificial intelligence

Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to Computer vision and artificial intelligence, image processing, and their related studies.

It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields.

Computer vision and artificial intelligence in medical image analysis applications

Real-time video fire/smoke detection based on CNN in antifire surveillance systems

In this paper, we present an intelligent fire/smoke detection approach based on YOLOv2 network, which aims to achieve high detection rate, low false alarm rate, and high speed. Such an algorithm has been tested with a state-of-the-art dataset plus a set of other not public videos.

1 and 2 deal with introduction and state-of-the-art video-based fire/smoke detectors. Section 4 discusses the global architecture and then each of the layers used in the video processing steps.

Fully connected layers were removed in YOLOv2, and instead, anchor boxes were used to predict bounding boxes. This idea was drawn from the state of art Faster R-CNN detector. YOLOv2 uses these anchors to detect the objects in the images. YOLOv2 anchor boxes are a set of rectangle boxes predefined with a specific height and width.

These boxes are used to capture the specific scale for the object to be detected. The size of anchor boxes is chosen based on the scale of bounding boxes in the training dataset. YOLOv2 with anchor boxes increases the output resolution for the networks’ convolutional layers. Anchor boxes can evaluate the prediction of all classes at once.

Many researches were focused on the traditional method of feature extraction for fire and smoke detection. The main problem for such techniques was time consumption for computing these feature extractions. This resulted in low performance and slow real-time for fire and smoke detection.

Motivated to the new development on the deep learning models, we proposed Fire and smoke detection based on a video camera using YOLOv2 model. Fire and smoke detection have a higher speed in imaging processing. In such a case, YOLOv2 is the best technique to encounter the detection of these objects.

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In this section, we will discuss the design for our YOLOv2 model. We used Deep Neural Designer tool in MATLAB to build YOLOv2 neural network layers. To establish a light-weight deep learning model to fit the embedded system, we constructed CNN with 21 layers, see Fig. This light-weight model is suitable for real-time performance, and it is worthy enough to be deployable on low-cost IoT devices.

The features extracted from this layer are given as input to YOLOv2 object detection subnetwork. YOLO2Layer subnetwork is used in this model, which creates YOLOv2 detection network. YOLOv2 Transform layer is used in our model to enhance the network stability for object localization. Finally, YOLOv2 output layer is used to refine the location of bounding boxes to the targeted objects.

To understand further, we carried experiments to compare our proposed method to the other object detectors such as R-CNN and Fast R-CNN. We used MATLAB with our bench-test dataset of fire and smoke videos. We run the three detectors simultaneously while calculating frames per second for each detector.

MATLAB Coder is used to generate the C Code from MATLAB to Jetson Nano. Parallel Computing Toolbox is utilized to solve the computational and data problems using a multicore processor and GPU. We also used Deep Learning Toolbox, which provides a framework for implementing the neural network with the algorithm.

GPU Coder Interface for Deep Learning Libraries customizes the generated code by utilizing a specific library in Jetson Nano. GPU Coder Support Package for NVIDIA GPU’s is used to generate and deploy the CUDA code. It enabled the communication remotely between MATLAB and NVIDIA on the targeted hardware.

It is an optimized tool that improves the code generation to the hardware precisely. We installed the required environment variables and applications such as JetPack Developer AI tool in Jetson Nano. This is to be applicable for code generation of our CNN detector from MATLAB. We also installed Microsoft visual studio 2019 as complier support for generating GPU code to Jetson Nano.

Biologically Inspired Methods for Imaging, Cognition, Vision, and Intelligence

This special issue focuses on biologically inspired or nature-driven approaches to computer vision. There were a total of 17 manuscripts received, five of which were accepted for publication. Two papers address computer vision problems: estimation of homographs between two different perspectives and computation of visual saliency.

Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques

Currently, research into the automatic and rapid detection of moulded grain is focused on the electronic nose, hyperspectral images and near-infrared spectrum technologies16,17,18,19. Although these technologies are effective and non-destructive testing methods, they still have some disadvantages compared to computer vision, such as being time-consuming and having lower locating abilities.

The structure of the BPNN model built to classify the mildew conditions of pitches is shown in Fig. The BPNN model has three layers: an input layer, a hidden layer and an output layer.

The structures of the CNN and DBN models are shown in Fig. The CNN model was composed of one input layer, two convolution layers, two pooling layers and one fully connected layer. The fully connected layer had two nodes for the result output. The DBN model was composed of one input layer of 675 nodes and four RBN layers.
Artificial Intelligence computer vision and artificial intelligence

The calculation time taken by the pitch segment recognition method combine with the different pitch-classification models to recognize the overall sample image was recorded. The calculation times of the pitch segment recognition method combine with the SVM, BPNN, CNN and DBN pitch-classification models were 139.

Conventional machine-learning models take much longer to recognize a single sample image compared with the deep learning model.

A uniform CNN model was built for the pitch classification of all five types of sample images. The accuracy rates of this CNN pitch-classification model of the training and testing sets for the five types of sample images are shown in Table 3. The average classification accuracy rate for pitches from all five types of sample images was 88.

The lowest accuracy rates were obtained for the classification of pitches from sample images with Aspergillus oryzae, but the accuracy rates were still above 85%. The accuracy rates of the uniform CNN model for pitch classification were slightly lower than those of the independent CNN model for each type of sample image. The recognition effects of the mould colony areas of the five types of sample image calculated with the uniform CNN model are shown in Fig.

Computer-vision technologies have been used to recognize mould colonies on unhulled paddy, specifically for mould species recognition and mould colony recognition and location.

The SVM model can obtain good results for many different classification tasks in agricultural detection areas21,22,23. In this paper, the SVM model also demonstrated good performance for infecting mould species recognition, which was only slightly lower than that of the DBN model. However, the classification performances of SVM models depend on the support vectors that are extracted from the input data24.

As the number of training samples increases, the number of support vectors in the SVM model also increases. When the number of training samples is large, the SVM will be complicated.

Threshold segmentation technology has been used for image segmentation in almost all computer vision detection areas, especially in the detection of agricultural products using computer vision. However, it is difficult to segment areas of interest from an image with both complex colour and texture by using threshold segmentation.

Mould colonies on unhulled paddies in an image have different colours and textures. The pitch segmentation recognition method has been used in remote-sensing images and medical image recognition25,26, which are also complicated image recognition tasks.

Deep learning techniques have also been used in hyperspectral imaging and speech recognition. Hu used the spectrum data of each pixel in a hyperspectral image and showed that the CNN model was also well able to classify 1-D array data28.

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In this paper, the model training and testing codes were written in the Matlab language and run on a Matlab platform without multicore computing technology. The Matlab language is an interpreted language and thus has a much slower running speed than a compiler language, such as C and C++.

Aspergillus nidulans, Aspergillus niger, Penicillum citrinum, Aspergillus oryzae and Aspergillus versicolor were used as the infection moulds in this study. These mould strains were purchased from the Guangdong microbiology culture center and stored in a refrigerator at 4  °C. Of these mould strains, Aspergillus nidulans, Penicillum citrinum and Aspergillus versicolor are mycotoxin-producing moulds and can produce Sterigmatocystin and Citrinin.

The five mould strains were twice activated at 28 °C for three days before inoculation. Then, the mould cells of the five strains were washed in five test tubes to make the mould suspensions. A blood cell counting plate was used to detect the cell concentrations of the five mould suspensions.

In each case, every measurement was replicated three times and an average value was determined. After that, every mould suspension was diluted to 106 CFU/g using sterile water, according to the original mould cell concentration.

To capture clear sample images of the mouldy unhulled paddy, a self-made computer vision system was used, as shown in Fig. The self-made computer vision system contained a digital camera, two strip-light sources, a camera support and a black base. Each strip-light source was 33 cm long, and contained 12 white LED tamps. The distances between the strip-light source and the base and between the strip-light sources were 15 cm and 20 cm, respectively.

The digital camera used was a Sony Nex-6 digital camera, and the lens was a Sony SELP1650. The shooting parameters were a focal length of 30 cm and an exposure time of 1/15 s. The images were captured at a resolution of 4912 × 3264 and saved in .

The SVM and BPNN models are classical non-linear classification models that are based on conventional machine-learning techniques and are widely used in image classification research. The CNN and DBN models are classical classification models based on deep-learning techniques. The CNN model is designed to classify original image data without pre-processing but is not suitable for high-resolution images. DBN was developed from BPNN but has a larger hidden-layer structure and possesses functional approximation ability. CNN and DBN are effective classification models but have not been widely used, especially in agricultural detection.

When segmenting pitches from the image, the size of each pitch segment should not be too large or small.

The model-built images taken from the pitch sample-extraction process were saved in jpg format and copied into the Microsoft Word 2016 software. A table with 30 rows, 30 columns and a uniform distribution of rows and columns was then created, and the table borders were aligned with the image borders. The image area in every table cell was taken as a pitch and manually recognized. Otherwise, the number ‘0’ was typed into the table cell, which indexed this pitch as a background pitch.

Conventional machine-learning technology requires characteristic parameter extraction for every pitch. r, g, b, stdr, stdg and stdb of the pitches in the training and the testing sets of each type of sample image were calculated using Matlab 2010b. r, g, b, stdr, stdg and stdb were then used as the input data, and the pitch manual recognition results were used as the output data. The SVM and BPNN models use for the pitch recognition of each type of sample image were built using LabSVM 3.

The R, G and B components of the pitch samples were used as the three input data matrices, and the manual recognition results were used as the output data.

The grey values of all pixels in each pitch were resized from a 15 × 15  × 3 data matrix to a 1-D data array with 675 elements. The data arrays of the sample pitches were used as the input data, and the manual recognition results were used as the output data.

developed the methods for image process and data process, built the classification models and wrote this manuscript.

Physician perspectives on integration of artificial intelligence into diagnostic pathology

Examining our data in retrospect, there are a number of important questions which should be considered by future investigators performing surveys of a similar nature. Understanding the perspectives of pathologists on how reimbursement schemas should adapt to the implementation of AI-based tools is of considerable importance to all stakeholders in the field. Surveying pathologists more specifically on how AI-tools could be integrated into their personal clinical practice would highlight areas of focus for developers and hospitals. Finally, the evolution of the relationship between histopathology and AI tools may be informed heavily by the availability of pathologists in a particular region or institution.

In conclusion, we present a survey of pathology colleagues around the world from a variety of demographic and practice backgrounds. Most respondents envision the eventual implementation of AI-tools as decision support tools used by human diagnosticians, not in place of.

is supported by the Richard Motyka Brain Tumour Research fellowship of the Brain Tumour Foundation of Canada. is supported by a Cancer Pathology Translational Research Grant of the Ontario Molecular Pathology Research Network.

Handwritten Character Classification and Recognition using Neural Network

Abstract Character recognition is the one of the emerging and developing techniques in the field of computer vision and artificial intelligence. Characters from a written document can be easily recognized by humans accurately. But the same task is difficult for a machin Different languages have different types of pattern i. Each character in a language is differing in their patterns, curves, shapes and orientation. For that we have to train that system to recognize a character. For the character recognition we process the input image, find its features, put classification scheme and train the system using neural network to recognize the character. For this mat lab image processing tool box and neural network tool box are used.

Character recognition is the conversion of image of handwritten or printed text into machine encoded text. The recognition of characters from scanned images of documents has been a problem that has received much attention in the fields of image processing, pattern recognition and artificial intelligence. In general, images are often corrupted by impulse noise in the procedures of image acquisition and transmission.

Hence, an efficient denoising technique becomes a very important issue in image processing. Many image denoising methods have been proposed to carry out impulse noise suppression .Some of them employ the standard median filter or its modifications However, these methods affect both noisy and noise-free pixels. To avoid the damage on noise-free pixels, an efficient filtering method is used.

Impulse noise present in the image in the form of black and white spots. Binarization process converts a gray scale image into a binary image.

In Segmentation sub divides an image into its constituent regions or objects. That is segmentation should stop when the objects or region of interest in an application have been detected. For segmentation different detection methods like point detection, line detection, edge detections are done.

chracters are trainned with artifical neural network and it gives an accuracy of 90%. This method is very useful at a great extent for better hand written character recognition.

Identification, classification and control: close ties analysed in reference to artistic practices in the heart of artificial intelligence

Philosopher at the National University of Colombia, with a Master’s in Communication from the Pontifical Xavierian University. Interested in subjects relating to image theory, computer vision and artificial intelligence.

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Currently, no public dataset for soybean charcoal rot disease classification is available. The applicability and success of supervised ML algorithms on predictive disease modeling have been reported but for other diseases and mainly based on image datasets.

In this work, specialized ML models were developed, for identification of charcoal rot disease by scrutinizing the symptoms of different parts of the soybean plants. In consequence of the lack of dataset for this disease, we have created our dataset; details of the dataset are provided in the dataset section. The main advantage of our proposed method is the identification of soybean charcoal rot disease at its early stage. A database of 2,000 soybean plants in natural field conditions was established. Supervised ML classifiers of LR-L1 LR-L2 MLP, RF, GBT, and SVM were trained to differentiate the healthy and infected soybean plants.

GBT was the best preforming classifier as it tries to sequentially improves the performance and also it includes the feature interactions in the learning. It could be due to our small data size as neural networks usually needs larger data size to perform well.

This paper investigated different ML algorithms for soybean charcoal rot disease detection and classification using morphological, physiological features. In this research effort, we presented an evaluation and comparison of six ML techniques on predicating charcoal rot disease. The results indicated that various ML techniques were slightly different in terms of their performance considering different evaluation metrics. Quantitative analysis of results indicated that GBT and SVM performed almost the same and demonstrated better performance compared with LR-L1, LR-L2, MLP, and RF approaches. Moreover, the feature ranking has shown the importance of including various features in the learning.

EK, SK, SR, and FG formulated the research problem and designed the approaches. All authors contributed to the final draft of the paper and approved the final version of the manuscript. EK, SK, and SR developed the processing workflow and performed the data analytics. All authors contributed to the writing and development of the manuscript.

Enhancement of search engines with image analysis

Common search engines like Google or Bing still just rely on text-based analysis, providing real time results with great accuracy. However, sometimes, the page rank implemented by these search engines does not provide the web-pages that we are looking for. Sergio Rodriguez-Vaamonde, researcher at Tecnalia Computer Vision Area, under the supervision of Prof.

Performed test on the TREC datasets reveal that the estimated precision of the search when incorporating this visual information is increased from 48.

Você é citado neste trabalho?

I am a PhD student at Image Sequence Evaluation group of Computer Vision Center, Universitat Autònoma de Barcelona. I am doing research in the field of Computer Vision, in particular, on Human Action Recognition from video.

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megaAI – 4K, 60 FPS camera solution for computer vision

py to see a live demonstration of MobileNetSSD being run on your host system.

Oral vs Poster vs Workshop. Which is the most prestigious in the context of Computer Vision and Artificial Intelligence?

This question is mainly about the perceived prestige of various events at conferences. Is there a hierarchy to it? Asking this in the perspective of being the presenter.

I am going to give you a partial answer, waiting for a more complete explanation of your question. The highest prestige is in giving an opening or closing speech at a plenary session in a big conference. Then a oral presentation at a parallel session in big confernence in your field, followed by oral/parallel/small. If you are the one leading the workshop, giving the tutorial, it’s a big thing.

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