Vasundhara Verma

Bio: Vasundhara Verma is an academic researcher. The author has contributed to research in topics: Canny edge detector & Edge detection. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

A Review on Edge detection Technique “Canny Edge Detection”

Abstract: The traditional Canny edge detection method is widely used in gray image processing. However, this traditional algorithm is unable to deal with color images and the parameters in the algorithm are difficult to be determined adaptively. In this paper, an improved Canny algorithm is proposed to detect edges in color image. The proposed algorithm is composed of the following steps: quaternion weighted average filter, vector Sobel gradient computation, non-maxima suppression based on interpolation, edge detection and connection. Experimental results show that the proposed algorithm outperforms other color image edge detection methods and can be widely used in color image processing. This project we present a two dimensional edge detector which gives the edge position in an image with sub-pixel accuracy. The method presented here gives an excellent accuracy (the position bias mean is almost zero and the standard deviation is less than one tenth of a pixel) with a low computational cost’ and its simple since it is derivated from the well known Non-Maxima Suppression method in Matlab[1].

Infrared computer vision in non-destructive imaging: Sharp delineation of subsurface defect boundaries in enhanced truncated correlation photothermal coherence tomography images using K-means clustering

Abstract: This work investigates the non-destructive detection of defects in thermal images of industrial materials based on segmentation of images generated using enhanced truncated-correlation photothermal coherence tomography (eTC-PCT). eTC-PCT is an active infrared thermography modality, which is being applied to the field of non-destructive testing (NDT) and in biomedical & dental thermophotonic imaging. In this report, we combine eTC-PCT with a computer vision algorithm to sharply delineate holes and manufacturing defects (cracks) inside industrial materials. To this end, the eTC-PCT reconstructed image is processed through three consecutive algorithm stages: A threshold selection filter is followed by filtered image segmentation using the K-means algorithm (clustering method) and the outcome is applied to the delineation of (otherwise blurred) discontinuity boundaries by means of the Canny edge detection algorithm. The role of each method is described and it is demonstrated that the combination of these three algorithms is optimal for achieving significant delineation enhancement (sharpness) of blind hole and crack boundaries in industrial materials.

Identification of Construction Era for Indian Subcontinent Ancient and Heritage Buildings by Using Deep Learning

Abstract: The Indian subcontinent is a south geographic part of Asia continent which consists of India, Bangladesh, Pakistan, Sri Lanka, Bhutan, Nepal, and Maldives. Different rulers or the empire of different periods have built various buildings and structures in these territories like Taj Mahal (Mughal Period), Sixty Dome Mosque (Sultanate Period), etc. From archaeological perspectives, a computational approach is very essential for identifying the construction period of the old or ancient buildings. This paper represents the construction era or period identification approach for Indian subcontinent old heritage buildings by using deep learning. In this study, it has been focused on the constructional features of British (1858–1947), Sultanate (1206–1526), and Mughal (1526–1540, 1555–1857) periods’ old buildings. Four different feature detection methods (Canny Edge Detector, Hough Line Transform, Find Contours, and Harris Corner Detector) have been used for classifying three types of architectural features of old buildings, such as Minaret, Dome and Front. The different periods’ old buildings contain different characteristics of the above-mentioned three architectural features. Finally, a custom Deep Neural Network (DNN) has been developed to apply in Convolutional Neural Network (CNN) for identifying the construction era of above-mentioned old periods.

Efficient mode decision scheme based on edge detection with Gaussian pulse for Intra-prediction in H.264/AVC

Abstract: Advanced Video Coding (H.264/AVC) is one of the most important video coding standards which was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG). Due to the complex procedures needed to find the optimal mode in intra prediction mode stage of H.264/AVC, an efficient mode decision is suggested in this paper. The main objectives of the proposed work are improving the compression efficiency of video coding and minimizing the degradation of video quality. The proposed algorithm is based on the gradient method for edge detection using the Prewitt filter to determine the texture direction of a block to be predicted in the luminance component of a video frame. To decide either implemented the DC mode or the edge-detection based gradient method to predict block under consideration, a predefined threshold value is used for the homogeneity test of each macro-block. To reduce the Bit Rate of the compressed video, Gaussian pulse modulation is also suggested to the Discrete Cosine Transformation (DCT) components of each macro-block during the coding process. The proposed algorithm is carried out on a luminance component of a set of different YUV video sequences with different resolutions and different quantization parameters using MATLAB. The suggested algorithm is evaluated using different metrics which are Peak Signal to Noise Ratio (PSNR) and bit rate measured by Bjontegaard Delta method. The simulation results show the ability of the proposed algorithm in enhancing the BDPSNR averagely by 1.14 dB and reducing the BDBR averagely by 11.34 %for QCIF sequences compared to the existing state-of-the-art fast intra-prediction mode decision algorithms.

Efficient mode decision scheme based on edge detection with Gaussian pulse for Intra-prediction in H.264/AVC

Abstract: Advanced Video Coding (H.264/AVC) is one of the most important video coding standards which was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG). Due to the complex procedures needed to find the optimal mode in intra prediction mode stage of H.264/AVC, an efficient mode decision is suggested in this paper. The main objectives of the proposed work are improving the compression efficiency of video coding and minimizing the degradation of video quality. The proposed algorithm is based on the gradient method for edge detection using the Prewitt filter to determine the texture direction of a block to be predicted in the luminance component of a video frame. To decide either implemented the DC mode or the edge-detection based gradient method to predict block under consideration, a predefined threshold value is used for the homogeneity test of each macro-block. To reduce the Bit Rate of the compressed video, Gaussian pulse modulation is also suggested to the Discrete Cosine Transformation (DCT) components of each macro-block during the coding process. The proposed algorithm is carried out on a luminance component of a set of different YUV video sequences with different resolutions and different quantization parameters using MATLAB. The suggested algorithm is evaluated using different metrics which are Peak Signal to Noise Ratio (PSNR) and bit rate measured by Bjontegaard Delta method. The simulation results show the ability of the proposed algorithm in enhancing the BDPSNR averagely by 1.14 dB and reducing the BDBR averagely by 11.34 %for QCIF sequences compared to the existing state-of-the-art fast intra-prediction mode decision algorithms.