Angular Histograms

This paper presents a study for the orientation effectiveness on the detected areas for many sampled objects when many type of the edges detection are applied. The Canny, Laplace, Prewitt and Sobel are applied for three objects (pencils' sharpeners with different colors). The MBR (Minimal Bounding Rectangular) are used to calculate the area in pixels, centroid and the orientation. The MR (Misclassification Ratio) is used to find the different between the edges detection techniques. The Canny edges detection technique gives the best result for the three used object using all orientations.

In high-stake situations, the micro-expressions reveal the hidden emotions of a person and it has potential applications in many areas. The recognition of such short-lived subtle expressions is a challenging task. The literature proposes several spatio-temporal features to encode the subtle changes on the face during a micro-expression. The spatial changes are almost indistinguishable as the facial appearance does not change appreciably. However, these changes possess a temporal pattern. This paper explores the temporal features associated with facial micro-movements and proposes fuzzy histogram of optical flow orientation (FHOFO) features for recognition of micro-expressions. The FHOFO constructs suitable angular histograms from optical flow vector orientations using histogram fuzzification to encode the temporal pattern for classifying the micro-expressions. We have also discussed the effect of inclusion and exclusion of the motion magnitudes during FHOFO feature extraction. It has been demonstrated by repeated experiments on the publicly available databases, that the performance of FHOFO is consistent and close or at times even better than the state-of-art techniques.

A new area of research to prevent illegal piracy and duplication copying is digital watermarking. Owing to personal computers being applied in many fields and Internet becoming popular and easier to use, most information is transmitted with digital format. Therefore, data copying and back up are more and easier in the World Wide Web and multimedia. The copyright and authentication gradually lose their security . How to protect intellectual property becomes important in technical study and research. Recently, the watermarking technique was proposed to solve the problem of protecting the intellectual property . The imperceptibility and robustness of watermarking can affect Watermarking embedded in frequency domain using FFT or DWT or DCT. In this paper, a watermark embedded in the host image by DCT transform has been proposed . There are several papers using the same manner to embed watermark into middle-band coefficients of DCT block but here we proposed a new approach to provide high security as compared to other developed watermarking tech. using image fusion of wavelet toolbox.

The multilayer secured DWT-DCT and YIQ color space based image watermarking technique with robustness and better correlation is presented here. The security levels are increased by using multiple pn sequences, Arnold scrambling, DWT domain, DCT domain and color space conversions. Peak signal to noise ratio and Normalized correlations are used as measurement metrics. The 512x512 sized color images with different histograms are used for testing and watermark of size 64x64 is embedded in HL region of DWT and 4x4 DCT is used. 'Haar' wavelet is used for decomposition and direct flexing factor is used. We got PSNR value is 63.9988 for flexing factor k=1 for Lena image and the maximum NC 0.9781 for flexing factor k=4 in Q color space. The comparative performance in Y, I and Q color space is presented. The technique is robust for different attacks like scaling, compression, rotation etc.

The offline signature verification is an automatic verification system that works on the scanned image of a signature. Signature verification uses the gray level measure with varying foreground features. The signature verification is performed by identifying feature vector using local patterns. The Local Binary Pattern (LBP) in signature verification has used to extract the local structure information by establishing the relationship between central pixel and adjacent pixels. This paper uses the Support Local Binary Pattern (SLBP) features for signature verification. The signatures are tested on MCYT dataset. The accuracy of the proposed method is tested against k-Nearest Neighbor Classifier (KNNC) and Linear Discriminant Classifier (LDC). KEYWORDS Histogram, Completed Local Binary Pattern (CLBP), Support Local Binary Pattern (SLBP).

Steganography is the science of embedding secret data inside data so they can be sent to destination security. Image steganography is the most popular type of carrier to hold information. Many algorithms have been proposed to hide information into digital images; the least significant bit algorithm (LSB) is one of these algorithms that is widely used in steganography. In this paper, we introduced propose steganography method by using edge detection of 6 bits in the image by edge detection and chaotic map. In work, we use 1-LSB or 2-LSB embedding, it depends on the threshold for edge detection image. We use locations not serialized of the pixel to embedding information rely on tent map.

The multilayer secured DWT-DCT and YIQ color space based image watermarking technique with robustness and better correlation is presented here. The security levels are increased by using multiple pn sequences, Arnold scrambling, DWT domain, DCT domain and color space conversions. Peak signal to noise ratio and Normalized correlations are used as measurement metrics. The 512x512 sized color images with different histograms are used for testing and watermark of size 64x64 is embedded in HL region of DWT and 4x4 DCT is used. 'Haar' wavelet is used for decomposition and direct flexing factor is used. We got PSNR value is 63.9988 for flexing factor k=1 for Lena image and the maximum NC 0.9781 for flexing factor k=4 in Q color space. The comparative performance in Y, I and Q color space is presented. The technique is robust for different attacks like scaling, compression, rotation etc.

A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this paper. This technique modifies the probability density function of an image by introducing constraints prior to the process of histogram equalization (HE). These constraints are formulated using two parameters which are optimized using swarm intelligence. This technique of contrast enhancement takes control over the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result to normalize the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. This approach provides a convenient and effective way to control the enhancement process, while being adaptive to various types of images. Experimental results show that th...

The purpose of this project in computer vision is to expand my knowledge of digital image processing through a review of mathematical techniques used by academic researchers in the work in multi-dimensional — i.e., colour — images. A brief review of colour imaging, as well as various uses of the assessment tools are reviewed. It is observed that many of the methods introduced in class: edges, Gaussian filters, augmentation, recognition, indexing, object identification– have considerations using HSV manipulation and with through histograms of such interpretations.

A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this paper. This technique modifies the probability density function of an image by introducing constraints prior to the process of histogram equalization (HE). These constraints are formulated using two parameters which are optimized using swarm intelligence. This technique of contrast enhancement takes control over the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result to normalize the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. This approach provides a convenient and effective way to control the enhancement process, while being adaptive to various types of images. Experimental results show that th...

A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this paper. This technique modifies the probability density function of an image by introducing constraints prior to the process of histogram equalization (HE). These constraints are formulated using two parameters which are optimized using swarm intelligence. This technique of contrast enhancement takes control over the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result to normalize the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. This approach provides a convenient and effective way to control the enhancement process, while being adaptive to various types of images. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.

This paper presents a study for the orientation effectiveness on the detected areas for many sampled objects when many type of the edges detection are applied. The Canny, Laplace, Prewitt and Sobel are applied for three objects (pencils' sharpeners with different colors). The MBR (Minimal Bounding Rectangular) are used to calculate the area in pixels, centroid and the orientation. The MR (Misclassification Ratio) is used to find the different between the edges detection techniques. The Canny edges detection technique gives the best result for the three used object using all orientations.

A novel Modified Histogram Equalization (MHE) technique for contrast enhancement is proposed in this paper. This technique modifies the probability density function of an image by introducing constraints prior to the process of histogram equalization (HE). These constraints are formulated using two parameters which are optimized using swarm intelligence. This technique of contrast enhancement takes control over
the effect of HE so that it enhances the image without causing any loss to its details. A median adjustment factor is then added to the result to normalize the change in the luminance level after enhancement. This factor suppresses the effect of luminance change due to the presence of outlier pixels. The outlier pixels of highly deviated intensities have greater impact in changing the contrast of an image. This approach
provides a convenient and effective way to control the enhancement process, while being adaptive to various types of images. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy and SSIM values than the existing histogram-based equalization methods.

Human activity recognition is an important task in computer vision because it has many application areas such as, healthcare, security, entertainment, and tactical scenarios. This paper presents a methodology to automatically recognize human activity from input video stream using Histogram of Oriented Gradient Pattern History (HOGPH) features and SVM classifier. For this purpose, the proposed system extracts HOG features from a sequence of consecutive video frames and analyzes them to construct HOGPH feature vector. The HOGPH feature vectors are used to train a multi-class SVM classifier for different human activities. In test mode, we use the classifier with HOGPH feature vector to recognize human activity. We have experimented with video data of human activity in real environments for three different tasks (browsing, reading, and writing). The experimental result and its accuracy reveal that the proposed system is applicable to recognize human activity in real-life.