Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum-rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance-based face recognition algorithms has been done at each level.

With the rapid development of computer science, problems with digital products piracy and copyright dispute become more serious; therefore, it is an urgent task to find solutions for these problems. In this study, the authors’ develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly using DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods.

A 3D model watermarking method robust to geometric attacks is proposed. The vertices of the model are classified into three groups. The vertices of the low-resolution group are used to establish an invariant space in which to resist geometric attacks. In the medium-resolution group, appropriate vertices in which to embed the watermarking information are selected. The selection of vertices that contain information about the watermark is based on the area of the local set of the vertex, the curvature of which determines the embedding strength of the watermark. The vertices of the high-resolution group are reserved for resisting simplification and smoothing attacks. The choice of the embedding position and the embedding strength can provide a suitable trade-off between good transparency and maximum robustness of the proposed method. The simulation results show that, compared to existing state-of-the-art methods, the proposed method is robust against attacks such as noise, smoothing, simplification, cropping, rotation, translation, and scaling while ensuring high visual quality of the watermarked model.

Pedestrian detection has become one of the hottest topics in intelligent traffic system because of its potential applications in driver assistance and automatic driving. In this study, a fast pedestrian detection and dynamic tracking method within vehicle-to-vehicle (V2V) cooperative environment is proposed. A dynamic tracking-by-detection framework for real-time pedestrian detection is developed. First, a cascade classifiers, based on selected Haar-like features, is trained to detect pedestrian. Then, CamShift algorithm combined with extended Kalman filtering is used to pedestrian dynamic tracking. Finally, with the crowdsourcing detected information, a smartphone-based V2V cooperative warning system is developed to share useful detection results within blind spots. The experiment results show that the proposed method has a real-time and accurate performance, which can provide a reference for road traffic safety monitoring technology.

Due to storage conditions and material's non-planar shape, geometric distortion of the two-dimensional content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large-scale digitisation. For large-scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large-scale digitisation. This study proposes an effective mesh based geometric restoration system (GRLSD) for large-scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML-based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface (GUI) toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state-of-the-art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large-scale digitalisation.

The ultimate receiver of image and video is human visual system (HVS). It is an important problem in the domain of image and video processing that how to establish visual information representation model meeting the HVS perception property. In this study, authors give theory analysis and experiment results to prove that l_1 norm-based entropy of primitive (EoP) is superior to the l_0 norm-based EoP for the monocular cue in image quality assessment. By developing the concept of mutual information of primitive (MIP) as the binocular cue, an l_1 EoP-based stereoscopic image quality assessment metric is proposed. With EoP as monocular cue and MIP as binocular cue, the relative entropy between the original stereoscopic image and the distorted one is explored to predict the quality score with support vector regression. To avoid destroying image's structured information, the structured EoP (SEoP) is further explored to measure the stereoscopic image information. Extensive experimental results demonstrate that the stereoscopic image quality assessment algorithm with SEoP as monocular cue and MIP as binocular cue outperforms many state-of-the-art ones.

Markov random field model (MRF) has attracted great attention in the field of image segmentation. Its basic unit can be pixels or regions. These pixel-based or region-based MRF models have their own advantages and disadvantages. In order to complement advantages of each other, a unified Markov random field (UMRF) model is proposed in this paper. The strength of the UMRF model lies in two aspects. First, the proposed model combines the benefits of the pixel-based and the region-based MRF models by decomposing the likelihood function into the product of the pixel likelihood function and the regional likelihood function. It can make the UMRF model take both pixel information and regional information into account. Second, a new regional feature is designed for the UMRF model to describe macro texture patterns. A principled probabilistic inference is developed to integrate different types of likelihood information and the spatial constraint by iteratively updating the posterior probability of the proposed model. Segmentation results can be achieved when iteration converges. Texture, remote sensing and nature images are employed to test the effectiveness of the proposed model. Experimental results illustrate that our model can achieve higher segmentation accuracy than either the pixel-based or region-based MRF models.

Image inpainting is used to restore some damaged or missing information regions of an image based on the surrounding information. Inspired by the fourth-order partial differential equation (PDE), this study presents two adaptive variational functionals for image inpainting, shows a stable variational scheme and investigates existence and uniqueness of solution for the minimising functionals. A consistent numerical discretisation scheme is also constructed for the derived fourth-order PDEs. The proposed models can simultaneously restore the missing, corrupted or undesirable regions and remove noise. Some successful image inpainting experiments and method comparisons are also provided in this study.

Over the past two years deep convolutional neural networks have pushed the performance of computer vision systems to soaring heights on semantic segmentation. In this study, the authors present a novel semantic segmentation method of using a deep fully convolutional neural network to achieve image segmentation results with more precise boundary localisation. The above segmentation engine is trainable, and consists of an encoder network with widening residual skipped connections and a decoder network with a pixel-wise classification layer. Here the encoder network with widening residual skipped connections allows the combination of shallow layer features and deep layer semantic features, and the decoder network with classification layer maps the low-resolution encoder features to full resolution image with pixel-wise classification. The experimental results on PASCAL VOC 2012 semantic segmentation dataset and Cityscapes dataset show that the proposed method is effective and competitive.

Intra-mode decision plays an important role in the new high-efficiency video coding (HEVC) video compression standard. The higher number of intra-modes in HEVC standard increased the computational complexity and encoding time significantly. To reduce the encoding time with as low effect as possible on the coding quality, a new algorithm based on the texture of the block diagonals is proposed. The proposed algorithm is used to reduce the number of calculations observed in the original standard. The results show that the proposed algorithm can show variety of solutions with wide range of encoding time reduction and BD rate increment. The solution with the highest time saving within an acceptable BD rate increment shows 40.85% time saving and 1.75% BD rate increment. With different parameters settings, BD rate increments with as low as 0.78% can be achieved while having 33.38% time saving.

The image compression performance of transform coders highly depends on the energy compaction (EC) capability of transforms. The popular transforms such as discrete cosine transform (DCT) and discrete wavelet transform (DWT) provide decent EC; however, their capability is not enough to deliver good quality image reconstruction for higher compression levels (CLs). In this study, the authors propose a new hybrid transform which is a fusion of wavelet packet transform (WPT) and block-DCT (BDCT) transform to achieve high-quality image compression. This new hybrid WPT-BDCT transform is able to attain higher EC than the existing transforms. Further, the authors have found a new energy compaction index (ECI) to evaluate the energy compactness of the image transforms. The proposed hybrid transform has been extensively evaluated, based on proposed ECI parameter, the visual quality assessment of reconstructed images and with the standard image quality indexes peak-signal-to-noise ratio and structural similarity index measure. It is reported that the proposed hybrid transform provides higher EC and outperforms the transforms namely DCT, DWT, WPT, multi-wavelet transform and existing hybrid transforms for all the CLs.

Vector quantisation (VQ) shows a good performance for image coding with high-compression ratios. However, there are many difficulties for image coding with VQ, especially the edge degradation and high-computational complexity. To resolve these two problems, the authors propose a new coding method based on edge orientation patterns (EOPs) by classifying image blocks into nine classes according to their edge orientations. For colour image coding, 27 codebooks (nine for each colour component) are pre-designed based on a series training images. In the encoding stage, an input colour image is decomposed into Y, Cb, and Cr components, and each component image is divided into non-overlapping 4 × 4 blocks. For each block, eight edge orientation templates of size 4 × 4 are performed to determine its edge orientation. According to the edge orientation, each block is compressed by using the corresponding codebook. Essentially, the authors’ scheme is a kind of classified VC (CVQ). Simulation results show that, their EOP-based CVQ can largely improve the compression efficiency as well as speeding up the encoding process and it is sufficient to establish effectiveness of the authors’ algorithm as compared with the existing techniques.

In this study, a novel algorithm is proposed for multi-focus image fusion based on multilevel morphological decomposition and classifier. The attractive feature of the algorithm is that it decomposes images into several layers with different morphological components, which makes it preserve more detail information of source images. In the algorithm, source images are first decomposed by the multilevel morphological component analysis. Then, feature vectors are extracted from nature layers, and they are classified by a trained two-class support vector machine. Then, consistency verification is employed to verify the decision matrix sets. Finally, coefficients are fused based on the decision matrix sets. Experimental results demonstrate the superiority of the proposed method in terms of subjective and objective evaluation.

This study presents hardware implementation of 5 × 5 median filter that uses a new low-latency median filter (LLMF) core in order to find the median of 25 integer values. The proposed LLMF core architecture computes the median of 25 integers in just three clock cycles. The maximum frequency of operation of the proposed median filter architecture is 394 MHz on the Xilinx Zynq FPGA device. The proposed LLMF core provides reduced clock cycle latency compared with the existing state-of-the-art median filter core architectures.