The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.

Pattern recognition is attracting the interest of researchers in the recently few years as a machine learning approaches due to its vast extending application areas. he application area includes communications, medicine, automations, data mining, military intelligence, document classification, bioinformatics, speech recognition and business. In this research convolutional neural networks (CNN) using for building system to recognize diseases that are happened in citrus. In this study presented dataset for seven classes of citrus diseases which contains 2450 images such as anthracnose, brown rot, citrus black spot, citrus canker, citrus scob, melanose and sooty mold citrus. The proposed system recognizes learned via CNN. The experimental result shows our model has ability to recognize citrus diseases with high and robustness accuracy. The study presented here gives 88% recognition of citrus diseases for the entire database.Povzetek: Konvolucijske nevronske mreže so uporabljene za detekcijo oz. klasifikacijo bolezni citrusov.

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In this research, different audio feature extraction techniques are implemented and classification approaches are presented to classify seven types of wind. We applied features techniques such as Zero Crossing Rate (ZCR), Fast Fourier Transformation (FFT), Linear predictive coding (LPC), and Perceptual Linear Prediction (PLP). We know that some of these methods are good with human voices, but we tried to apply them here to characterize the wind audio content. The CNN classification method is implemented to determine the class of input wind sound signal. Experimental results show that each of these extraction feature methods give different results, but classification accuracy that are obtained by using PLP features return the best results. Povzetek: V tej raziskavi se izvajajo različne tehnike ekstrakcije zvočnih funkcij in predstavljeni so klasifikacijski pristopi za razvrščanje sedmih vrst vetra. Kjer smo uporabili tehniko funkcij, kot so Zero Crossing Rate (ZCR), Fast Fourier Transformation (FFT), Linear Prediction Coding (LPC), Perceptual Linear Prediction (PLP). Vemo, da nekatere od teh metod dobro vplivajo na človeške glasove, vendar smo jih poskušali uporabiti tukaj za označevanje zvočne vsebine vetra. Za določitev razreda vhodnega zvočnega signala vetra je uporabljena klasifikacijska metoda CNN. Eksperimentalni rezultati kažejo, da je vsaka od teh metod ekstrakcijskih lastnosti dala različne rezultate, vendar se je za klasifikacijo lastnosti PLP izkazalo, da imajo najboljše rezultat.

Image processing and computer vision have a major role in addressing many problems, where images and techniques that are dealt with them contribute greatly to finding solutions to many topics and in different directions. Classification techniques have a large and important role in this field, through which it is possible to recognize and classify images in a way that helps in solving a specific problem. Among the most prominent models that are distinguished for their ability and accuracy in distinguishing is the CNN model. In this research, we have introduced a system to classify the sea coral images because sea coral and its classes have many benefits in many aspects of our lives. The important thing in this work is to study four CNN architectures model (i.e., AlexNet, SqueezeNet, to determine the accuracy and efficiency of these architectures and determine the best of them with coral image data, and we are shown the details in the research paragraphs. The results showed 83.33% accuracy for AlexNet, 80.85% SqueezeNet, 90.5% GoogLeNet and 93.17% for Inception-v3.Povzetek: Predstavljena je uporaba arhitektur konvolucijskih nevronskih mrež (CNN) za razvrščanje slik morskih koral.