In the fields of Computer Vision, Image Semantic Segmentation is one of the most focused research areas. These are widely used for several real-time problems for finding the foreground or background scenes of a given image or a video.  Initially, it is achieved using computer vision techniques, later once the deep learning is in its rise, ultimately it took over the entire image classification and segmentation techniques. These are widely surveyed and reviewed as they are used in several Image Processing, Feature Detection and Medical Fields. All the models for implementing Image Segmentation are mostly done using a specific neural network architecture called a convolution neural network. In this work, firstly we'll study the implementation of Image Segmentation models and advantages, disadvantages over one another including their development trends. We'll be discussing all the models and their applications concerning other fancy methods that are mostly used which involves hyperparameters and the transitive comparison between them. 

Now a days application of sparse representation are widely spreading in many fields such as face recognition. For this usage, defining a dictionary and choosing a proper recovery algorithm plays an important role for the method accuracy. In this paper, two type of dictionaries based on input face images, the method named SRC, and input extracted features, the method named MKD-SRC, are constructed. SRC fails for partial face recognition whereas MKD-SRC overcomes the problem. Three extension of MKD-SRC are introduced and their performance for comparison are presented. For recommending proper recovery algorithm, in this paper, we focus on three greedy algorithms, called MP, OMP, CoSaMP and another called Homotopy. Three standard data sets named AR, Extended Yale-B and Essex University are used to asses which recovery algorithm has an efficient response for proposed methods. The preferred recovery algorithm was chosen based on achieved accuracy and run time.

Remote sensing image classification is very much essential for many socio, economic and environmental applications in the society. They aid in agriculture monitoring, urban planning, forest monitoring, etc. Classification of a remote sensing image is still a challenging problem because of its multifold problems. A new algorithm LCDFOSCA (Linear Contact Distribution First Order Statistics Classification Algorithm) is proposed in this paper to extract the texture features from a Color remote sensing image. This algorithm uses linear contact distributions, mathematical morphology, and first-order statistics to extract the texture features. Later k-means is used to cluster these feature vectors and then classify the image. This algorithm is implemented on NRSC ‘Tirupathi’ area 2.5m, 1m color images and on Google Earth images. The algorithm is evaluated with various measures like the dice coefficient, segmentation accuracy, etc and obtained promising results.

Bangla vehicle number plate recognition is still an unsolved research issue for efficient investigation of unregistered vehicles, traffic observation, management and most importantly for Intelligent Transportation System (ITS). Previous research on vehicle plate recognition have been suffering various challenges, i.e. capturing high level image from moving vehicles, number plate with complicated background, detection from different tilt and angle, detection in different lightening conditions and weather conditions, recognition of doubtful and ambiguous signs in road time road scenario. The main aim of this research is to provide critical analysis on various perspectives of vehicle plate recognition, i.e. Extraction of vehicle plates from vehicle, Segmentation of characters and finally, Recognition of segmented characters. At first, this research illustrates comprehensive reviews on existing methods. After that, existing frameworks are analyzed based on overall advantages and disadvantages for each steps in the previous research. Finally, extensive experimental validation is depicted in five aspects, i.e. method, accuracy, processing time, datasets and relevancy with real time scenario. Proposed comprehensive reviews are expected to contribute significantly to perform efficient vehicle plate recognition in Intelligent Transportation System (ITS) research.

Dynamic Texture Analysis is a hotspot field in Computer Vision. Dynamic Textures are temporal extensions of static Textures. There are broadly two cat-egories of Dynamic Textures: natural and manmade. Smoke, fire, water and tree are natural while traffic and crowd are manmade Dynamic Textures. In this paper, an integrated efficient algorithm is discussed and proposed which is used for detecting two features of objects in Dynamic Textures namely, velocity and orientation. These two features can be used in identifying the velocity of vehicles in traffic, stampede prediction and cloud movement direction. Optical flow technique is used to obtain the velocity feature of the objects in motion.  Since optical flow is computationally complex, it is applied after background subtraction. This reduces the number of computations. Variance feature of Gabor filter is used to find the orientation which gives direction of movement of majority objects in a video. The combination of optical flow and Gabor filter technique together gives accurate orientation and velocity of Dynamic Texture with less number of computations in terms of time and algorithm.. Proposed algorithm can be used in real time applications. Velocity detection is done using Farneback Optical flow and orientation or angle detection is done using Bank of Gabor Filters The existing methods are used to calculate either velocity or orientation accurately individually. Varied datasets are used for experimentation and acquired results are validated for the selected database. 

Face recognition is one of the conventional problems in computer vision. Its recognition rate falls steeply when the images are captured in an unconstrained environment. One of the fundamental issues that creep into unconstrained environment capturing is that of the face pose variation. Due to face pose variation, occlusion of crucial features takes place. Occlusion may lead to information loss in the face descriptor which describes the face appearance. In this paper, we propose learning-based descriptor that combines horizontal, vertical and diagonal pattern of blocks generated from the convolution of face image with Gabor filter bank. To use only discriminative features, Probabilistic Linear Discriminant Analysis (PLDA) is used. The fusion of non-uniform texture based descriptor along with the PLDA approach aids in retaining enough of discriminative information to overcome the information loss occurring during feature occlusion. Since HVDGBP face descriptor utilizes the fundamental concept of Linear Binary Pattern (LBP) henceforth it helped in meeting low processing demands and ease of computing characteristic required for good face descriptors. Comprehensive comparative performance analysis of the robustness of the proposed face descriptor to withstand pose variations is presented. UMIST and AT&T Database is used for experimental analysis.