Contourlet based multi-exposure image fusion with compensation for multi-dimensional camera shake

Multi-exposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of limited dynamic range by rendering multiple images captured at different exposure settings. One practical problem overlooked by existing algorithms is the compensation required for image deregistration due to possible multi-dimensional camera shake that results within the time gap of capturing the multiple exposure images. In our approach RANdom SAmple Consensus (RANSAC) algorithm is used to identify inliers of key-points identified by the Scale Invariant Feature Transform (SIFT) approach subsequently to the use of Coherent Point Drift (CPD) algorithm to register the images based on the selected set of key points. We provide experimental results on set of images with multi-dimensional (translational and rotational) to prove the proposed algorithm's capability to register and fuse multiple exposure images taken in the presence of camera shake providing subjectively enhanced output images

Novel approach to enhance face recognition using depth maps

Face recognition, although being a popular area of research and study, still has many challenges, and with the appearance of the Microsoft Kinect device, new possibilities of research were uncovered, one of which is face recognition using the Kinect. With the goal of enhancing face recognition, this paper is aiming to prove how depth maps, since not effected by illumination, can improve face recognition with a benchmark algorithm based on the Eigenface. This required some experiments to be carried out, mainly in order to check if algorithms created to recognize faces using normal images can be as effective if not more effective with depth map images. The OpenCV Eigenface algorithm implementation was used for the purpose of training and testing both normal and depth-map images. Finally, results of the experiments are presented to prove the ability of the tested algorithm to function with depth maps, also, proving the capability of depth maps face recognition’s task in poor illumination

Rate-controlled, region-of-interest-based image coding with JPEG-LS

Since the standardization of JPEG-LS, several improvements and variations to the original algorithms have been proposed. In this paper we propose a Region of Interest (ROT) based coding strategy for JPEG-LS that has the additional ability of providing effective rate controlled image compression. Given a single ROT or multiple ROTs of a fixed or arbitrary shape, the scheme we propose is able to compress a given image by a required ratio, whilst maintaining the subjective image of the ROTs at either lossless or at a quality specified by a Target Compression Ratio ( TCR) of the ROT. We provide experimental results to compare the performance of the proposed rate-control algorithm with the state of the art near lossless rate control schemes. We show that the proposed scheme is able to achieve much higher TCRs, at increased accuracy and better objective image quality, using a less computationally intensive rate control algorithm. Finally we demonstrate that the proposed ROT based coding scheme can be used to extend the applicability of JPEG-LS to medical and satellite imaging applications and provides a useful alternative to JPEG-2000 based ROT coding

Vehicle make and model recognition in CCTV footage

This paper presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD (coherent Point Drift) is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR (Vehicle Make and Model Recognition) task and may capture vehicles at different approaching angles. Also a novel ROI (Region Of Interest) segmentation is proposed. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximize the reliability of the fnal outcome. Experimental results are provided to prove that the proposed system demonstrates accuracy over 95% when tested in real CCTV footage with no prior camera calibration

Video forensics in cloud computing: the challenges & recommendations

Forensic analysis of large video surveillance datasets requires computationally demanding processing and significant storage space. The current standalone and often dedicated computing infrastructure used for the purpose is rather limited due to practical limits of hardware scalability and the associated cost. Recently Cloud Computing has emerged as a viable solution to computing resource limitations, taking full advantage of virtualisation capabilities and distributed computing technologies. Consequently the opportunities provided by cloud computing service to support the requirements of forensic video surveillance systems have been recently studied in literature. However such studies have been limited to very simple video analytic tasks carried out within a cloud based architecture. The requirements of a larger scale video forensic system are significantly more and demand an in-depth study. Especially there is a need to balance the benefits of cloud computing with the potential risks of security and privacy breaches of the video data. Understanding different legal issues involved in deploying video surveillance in cloud computing will help making the proposed security architecture affective against potential threats and hence lawful. In this work we conduct a literature review to understand the current regulations and guidelines behind establishing a trustworthy, cloud based video surveillance system. In particular we discuss the requirements of a legally acceptable video forensic system, study the current security and privacy challenges of cloud based computing systems and make recommendations for the design of a cloud based video forensic system

Improvements to JPEG-LS via diagonal edge-based prediction

JPEG-LS is the latest pixel based lossless to near lossless still image coding standard introduced by the Joint Photographic Experts Group (JPEG) '. In this standard simple localized edge detection techniques are used in order to determine the predictive value of each pixel. These edge detection techniques only detect horizontal and vertical edges and the corresponding predictors have only been optimized for the accurate prediction of pixels in the locality of horizontal and/or vertical edges. As a result JPEG-LS produces large prediction enors in the locality of diagonal edges. In this paper we propose a low complexity, low cost technique that accurately detects diagonal edges and predicts the value of pixels to be encoded based on the gradients available within the standard predictive template of JPEG-LS. We provide experimental results to show that the proposed technique outperforms JPEG-LS in terms of predicted mean squared error, by a margin ofup to 8.5 1%

Real-time multi barcode reader for industrial applications

The advances in automated production processes have resulted in the need for detecting, reading and decoding 2D datamatrix barcodes at very high speeds. This requires the correct combination of high speed optical devices that are capable of capturing high quality images and computer vision algorithms that can read and decode the barcodes accurately. Such barcode readers should also be capable of resolving fundamental imaging challenges arising from blurred barcode edges, reflections from possible polyethylene wrapping, poor and/or non-uniform illumination, fluctuations of focus, rotation and scale changes. Addressing the above challenges in this paper we propose the design and implementation of a high speed multi-barcode reader and provide test results from an industrial trial. To authors knowledge such a comprehensive system has not been proposed and fully investigated in existing literature. To reduce the reflections on the images caused due to polyethylene wrapping used in typical packaging, polarising filters have been used. The images captured using the optical system above will still include imperfections and variations due to scale, rotation, illumination etc. We use a number of novel image enhancement algorithms optimised for use with 2D datamatrix barcodes for image de-blurring, contrast point and self-shadow removal using an affine transform based approach and non-uniform illumination correction. The enhanced images are subsequently used for barcode detection and recognition. We provide experimental results from a factory trial of using the multi-barcode reader and evaluate the performance of each optical unit and computer vision algorithm used. The results indicate an overall accuracy of 99.6 % in barcode recognition at typical speeds of industrial conveyor systems

An extended H.264 CODEC for stereoscopic video coding

We propose an extension to the H.264 video coding standard, which is capable of efficiently coding stereoscopic video sequences. In contrast to previous techniques, the proposed Stereoscopic Video CODEC uses a single modified H.264 encoder and a single modified H.264 decoder in its design. The left (reference) and right (predicted) sequences are fed alternatively to the encoder. The modified H.264 encoder uses a Decoded Picture Buffer Store (DPBS) in addition to the regular DPB of the original H.264 encoder. An effective buffer management strategy between DPBS and DPB is used so that the left sequence frames are coded only based on its previously coded frames while the right frames are coded based on previously coded frames from both left and right sequences. We show that the proposed CODEC has the capability of exploiting worldline correlation present in stereo video sequences, in addition to the exploitation of joint spatialtemporal- binocular correlation. Further we show that the coded bit stream fully conforms to a standard H.264 bit-stream and a standard H.264 decoder will be able to effectively decode the left video stream ignoring the right. We provide experimental results on two popular test stereoscopic video sequences to prove the efficiency of the proposed CODEC

Localised contourlet features in vehicle make and model recognition

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance

Securing DICOM images based on adaptive pixel thresholding approach

This paper presents a novel efficient two-region Selective encryption approach that exploits medical images statistical properties to adaptively segment Digital Imaging and Communications in Medicine (DICOM) images into regions using thresholding technique in the spatial domain. This approach uses adaptive pixel thresholding, in which thresholds for same DICOM modality, anatomy part and pixel intensities' range were extracted off-line. Then, the extracted thresholds were objectively and subjectively evaluated to select the most accurate threshold for the correspondent pixel intensities' range. In the on-line stage, DICOM images were segmented into a Region Of Interest (ROI) and a Region Of Background (ROB) based on their pixels intensities using the adopted thresholds. After that, ROI was encrypted using Advanced Encryption Standard (AES), while ROB was encrypted using XXTEA. The main goal of the proposed approach is to reduce the encryption processing time overhead in comparison with the Naïve approach; where all image pixels are encrypted using AES. The proposed approach aims to achieve a trade-off between processing time and a high level of security. The encryption time of the proposed approach can save up to 60% of the Naïve encryption time for DICOM images with small-medium ROI