Amit Ujlayan

An Exploration in Perception-Based Digital Media Processing: A Psychological Perspective

The computer vision field deals with the problem of understanding the scene or features in images of real world with the help of image processing and pattern recognition techniques. The main complication in this task is that the objects present in the images may have different appearances to the camera due to illumination effects, camera position, shadows, types of camera, etc. Nevertheless, with the advancement of technologies, today computer vision has provided reliable methods for various tasks like object classification, action recognition, autonomous driving, scene analysis, highlights extraction in videos and many more. But the problem of automatic qualifying is that how well people perform these actions has been largely unexplored. Human visual system and cognition can outperform the performance of computer vision algorithms. The objective of this paper is to highlight the state of the art of various psychological views of human visual perception in computer vision methods that have been found to operate well and that led up to the above-mentioned capabilities.

From Cognitive Psychology to Image Segmentation: A Change of Perspective

Image segmentation is a complex and essential task used in many computer vision applications. The problem of image segmentation can essentially be formulated as a grouping problem which in its simplest form tries to group the pixels of image into distinguished regions of interest so that further processing of the extracted regions can be achieved. This work proposes an image segmentation model which is inspired by the findings in cognitive psychology theories to divide the image into separate coherent regions. The proposed work tries to correlate between human and machine cognition by studying the segmentation process under the light of psychology of human vision.

Comparative analysis of dimensionality reduction algorithms, case study: PCA

On the basis of the evaluation of local properties of the data many nonlinear techniques have been suggested the field of computer vision. The application of the dimensionality reduction covers many fields like medical, geographical, simulation and many more. I have studied MDS, LLE and LTSA. Overall, the users are allowed to access the search-tools in linear system. A review and systematic comparison of all the existing techniques has been presented in this paper. The outputs have been explained through identification of current non-linear techniques, and suggestions pertaining to the way the performance of nonlinear dimensionality reduction techniques can be improved. The Purpose of this idea is based on the to implement it in manifold fields by analyzing the result of face detector and recognizer for multiple people in real time with Principal Component analysis on eigen face. According to the most recent research, some issues are confronted in the security at public places. The efficiency and accuracy of these problems can be improved with the range and intricacy of camera networks are booming and the audited surroundings have become more and more entangled and crowded. How these emerging challenges are faced is discussed in the paper.

GPS enabled efficient sound source localisation using microphone array

In this era of rapid technical developments in the field of sound source localisation, there is an urgent need to integrate these individual technologies to create an accurate and more power localiser. When the technique volumetric steered response power (V-SRP) is compared to steered response power (SRP), the former achieves a significant lower computational complexity. This is achieved without much decrease in the accuracy of location estimation. By appending a fine search stem to V-SRP, a refined version (RV-SRP) is achieved with improved compromise of complexity and accuracy. Experiments are performed using both the simulated as well as real scenario data to demonstrate the decrease in the computational cost. Further, in the experiment, the global positioning system (GPS) provides the accurate location coordinates of the source. It is to report newly developed system and methodologies which will be able to find approximate azimuth and elevation angle of sound source with integration of TDE-ILD-HRTF along with geo-positioning visualisations to help humanity to achieve its goal towards peace and harmony.

An efficient SOM and EM-based intravascular ultrasound blood vessel image segmentation approach

Intravascular ultrasound (IVUS) is a catheter-based imaging method used in the study of atherosclerotic disease. IVUS produces cross-sectional images of the blood vessels that enable quantitative assessment of the plaque. Automatic segmentation of the anatomical structures in the IVUS image is a really challenging task due to the presence of noise and catheter artifacts. Hence, this paper presents an efficient self-organizing map (SOM) and expectation-maximization (EM)-based approach for the segmentation of cross-sectional view of the IVUS blood vessel image. In our proposed work, the directional filtering is used to improve the signal to noise ratio of the blood vessel image. The Hough transform is used for predicting the circle in the image. Segmentation of the image is performed using the SOM and EM algorithm. After the segmentation process, extraction of the common pixels is performed. Gray-level co-occurrence matrix is applied for extracting features from the image. Fuzzy-relevance vector machine based classification of the image is performed. From the comparison results, it is clearly observed that the proposed approach is highly efficient than the existing techniques.

ATP-MLSM: angular texture pattern-multi-level set model-based retinal image segmentation approach

This paper proposes an Angular Texture Pattern (ATP)-Multi-Level Set Model (MLSM)-based retinal image segmentation approach. The location of Optical Disk (OD) is estimated by initially collecting the blood vessel region from the retinal image. Based on the identification of OD location, the bright pixel values are estimated to provide the boundary detail of OD. From this boundary detail, the Region of Interest (ROI) such as Hard Exudates (HE) is obtained in the binary form, to enable contour formation for the OD and HE. Then, the cup-to-disk ratio of the OD is calculated, and the number of HEs is counted. The severity level of the DR and Glaucoma is determined based on the cup-to-disk ratio of the OD and HE count value. The proposed algorithm is tested by using the retinal images of the DIARETDB1 and MESSIDOR database. The proposed approach achieves better performance than the existing OD segmentation methodologies.