The human visual system (HVS) can reliably perceive salient objects in an image, but, it remains a challenge to computationally model the process of detecting salient objects without prior knowledge of the image contents. This paper proposes a visual-attention-aware model to mimic the HVS for salient-object detection. The informative and directional patches can be seen as visual stimuli, and used as neuronal cues for humans to interpret and detect salient objects. In order to simulate this process, two typical patches are extracted individually and in parallel from the intensity channel and the discriminant color channel, respectively, as the primitives. In our algorithm, an improved wavelet-based salient-patch detector is used to extract the visually informative patches. In addition, as humans are sensitive to orientation features, and as directional patches are reliable cues, we also propose a method for extracting directional patches. These two different types of patches are then combined to form the most important patches, which are called preferential patches and are considered as the visual stimuli applied to the HVS for salient-object detection. Compared with the state-of-the-art methods for salient-object detection, experimental results using publicly available datasets show that our produced algorithm is reliable and effective.

Visual-Patch-Attention-Aware Saliency Detection

The aim of this paper was to develop a region based active contour model and Fuzzy C-Means (FCM) technique for segmentation of lung nodules. Ultimately, detection and assisted diagnosis of nodules at earlier stage increase the mortality rate. Among many imaging modalities, Computed Tomography (CT) is being the most sought because of its imaging sensitivity, high resolution and isotropic acquisition in locating the lung lesions. The proposed methodology focuses on acquisition of CT images, reconstruction of lung parenchyma and segmentation of lung nodules. Reconstruction of parenchyma can be employed using selective binary and Gaussian filtering with new signed pressure force function (SBGF-new SPF) and clustering technique was used for nodule segmentation. Comparative experiments demonstrate the advantages of the proposed method in terms of decreased error rate and increased similarity measure.

Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering

https://researchportal.port.ac.uk/portal/files/11764690/Principal_Local_Color_Contrast_postprint.pdf

Saliency detection based on directional patches extraction and principal local color contrast

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.

/pdf/image-segmentation-using-information-bottleneck-method-2fbce8bpan.pdf

Image Segmentation Using Information Bottleneck Method

A top-down region dividing approach for image segmentation

Image simplification is an important pre-processing task in object segmentation and recognition. The histogram-based approach is the simplest way of image simplification. However, it is difficult to find a general rule of obtaining the proper threshold for all different cases. Recently, the research on image simplification using morphology seems to achieve a better performance. Nevertheless, the quality of simplification results relies on the selection of maskers obtained from complicated morphological operations. In this paper, we present a new perspective view based on differential histogram equalization (DHE) in image simplification. It is developed to not only generate better quality results, but possess lower computational complexity.

Differential Histogram Equalization Based Image Simplification

We present a new robust high-capacity watermarking algorithm using the block-based chaotic map and the intersection-based pixels collection. The block-based chaotic map is created to break the local spatial similarity of an image partially. The intersection-based pixels collection is utilized to generate a container and a reference register which is used to indicate the location of the container for watermark embedding and extracting. Experimental results demonstrate that our algorithm can achieve robust high-capacity performance in comparisons with the current watermarking algorithm. It can embed more than 5,050 bits of information in an image of size 202 times 202. It can achieve a low message error rate of less than 0.22 even under various attacks such as additive noise, low-pass filtering, and lossy compression

Yih-Tyng Wu

Papers

A top-down region dividing approach for image segmentation

Differential Histogram Equalization Based Image Simplification

A Robust High-Capacity Watermarking Algorithm