SIMULATION OF EDGE DETECTION SYSTEMS

INTRODUCTION:

Edge detection is a fundamental tool used in most image processing applications to obtain information from the frames before feature extraction and object segmentation. This process detects outlines of an object and boundaries between objects and the background in the image. Beyond that, Edge Detection refers to the process of identifying and locating sharp discontinuities in intensities in an image. The discontinuities are abrupt changes in pixels intensity which characterize boundaries of objects in a scene structure. This process significantly reduces the amount of date in the image, while preserving the most important structural feature of that image. Edge detection is considered to be the ideal algorithm for images that are corrupted with white noise. The Edge is characterized by its height, slope angle,and horizontal coordinate of the slope midpoint. An ideal Edge Detector should  produce an edge indication localized to a single pixel located at the midpoint of the slope.There are many ways to perform Edge detection. However, the majority of different methods may be grouped into two categories, gradient and Laplacian. The basic Edge detection operator is a matrix area gradient operation that determines the level of variance between different pixels. The edge detection operator is calculated by forming a matrix centered on a pixel chosen as the centre of the matrix area. If the value of the matrix area is above a given threshold, then the middle pixel is classified as an edge. Examples of gradient based edge detectors are Roberts, Prewitt and Sobel operators. All the gradient –based algorithms have Kernel operators that calculate the strength of the slope in directions that are orthogonal to each other, generally horizontal and vertical.

The requirements that the algorithms must meet are:

a) Show the effectiveness and the noise resistance for remote sensing image.

b) Satisfying real time-constraints, and minimizing hardware resources in order to meet embedding requirements.

c) Significantly reducing the amount of date and filters out useless information.

Classically, Edge detection algorithms are implemented on software. With advances in the VLSI technology hardware implementation has become an attractive alternative. Assigning complex computation tasks to hardware and exploiting the parallelism and pipelining in algorithm yield significant speedup in running times. Implementation image processing on reconfigurable hardware minimizes the time-to-market cost, enables rapid prototyping of complex algorithm and simplifies debugging and verification.

APPLICATIONS:

a) Brillouin frequency shift distribution in fibre sensors based on double-technique.

b) Progressive Edge Detection on multi-bit images using polynomial-based binarization.

c) Application of an edge detection method to satellite images for distinguishing sea surface temperature fronts near janpanese coast.

d) An algorithm of sub-pixel edge detection based on ZOM and application in calibration for robot vision.

e) It has some more wide applications such as 3D reconstruction, recognition, image enhancement, image restoration and compression.

VIDEO DEMO

DESIGN AND IMPLEMENTATION OF AN FPGA-BASED REAL-TIME VERY LOW RESOLUTION FACE RECOGNITION SYSTEM

Very Low Resolution Face Recognition Problem

This both projects addresses the very low resolution (VLR) problem in face recognition in which the resolution of the face image to be recognized is lower than 16 16. With the increasing demand of surveillance camera-based applications, the VLR problem happens in many face application systems.

Existing face recognition algorithms are not able to give satisfactory performance on the VLR face image. While face super-resolution (SR) methods can be employed to enhance the resolution of the images, the existing learning-based face SR methods do not perform well on such a VLR face image. To overcome this problem, this project proposes a novel approach to learn the relationship between the 

high-resolution image space and the VLR image space for face 

SR.

Based on this new approach, two constraints, namely, new data and discriminative constraints, are designed for good visuality and face recognition applications under the VLR problem, respectively. Experimental results show that the proposed SR 

algorithm based on relationship learning outperforms the existing 

algorithms in public face databases.

Design files can be downloaded from below link and for understanding purpose

DOWNLOAD FILES 

DEMO VIDEO

A VLSI ARCHITECTURE FOR VISIBLE WATERMARKING IN A SECURE STILL DIGITAL CAMERA (S2DC) DESIGN (CORRECTED)

INTRODUCTION

WATERMARKING is the process that embeds data called a watermark, a tag, or label into a multimedia object such that the watermark can be detected or extracted later to make an assertion about the object. The object may be an image, audio, video, or text. Whether the host data is in spatial domain, discrete cosine-transformed, or wavelet-transformed, watermarks of varying degree of visibility are added to present media as a guarantee of authenticity, ownership, source, and copyright protection. In general, any watermarking scheme (algorithm) consists of three parts, such as the following: 

1) watermark;

2) encoder (insertion algorithm);

3) decoder and comparator (verification or extraction or detection algorithm)

Whether each owner has a unique watermark or an owner wants to use different watermarks in different objects, the marking algorithm incorporates the watermark into the object. The verification algorithm authenticates the object determining both the owner and the integrity of the object. Watermarks and watermarking techniques can be divided into various categories. The watermarks can be applied either in spatial domain or in frequency domain. It has been pointed out that the frequency-domain methods are more robust than the spatial-domain techniques. On the other hand, the spatial domain watermarking schemes have less computational overhead compared with frequency-domain schemes. According to human perception, the digital watermarks can be divided into four categories:

1) visible; 

2) invisible-robust;

3) invisible-fragile;

4) dual. 

A visible watermark is a secondary translucent image overlaid into the primary image and appears visible to a casual viewer on careful inspection. The invisible-robust watermark is embedded in such a way that modifications made to the pixel value is perceptually not noticed, and it can be recovered only with appropriate decoding mechanism. The invisible-fragile watermark is embedded in such a way that any manipulation or modification of the image would alter or destroy the watermark. A dual watermark is a combination of a visible and an invisible watermark. In this type of watermark, an invisible watermark is used as a back-up for the visible watermark. 

VIDEO DEMO