Multimodal Computational Attention for Scene Understanding

Robotic systems have limited computational capacities. Hence, computational attention models are important to focus on specific stimuli and allow for complex cognitive processing. For this purpose, we developed auditory and visual attention models that enable robotic platforms to efficiently explore and analyze natural scenes. To allow for attention guidance in human-robot interaction, we use machine learning to integrate the influence of verbal and non-verbal social signals into our models

On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection

It has become apparent that a Gaussian center bias can serve as an important prior for visual saliency detection, which has been demonstrated for predicting human eye fixations and salient object detection. Tseng et al. have shown that the photographer's tendency to place interesting objects in the center is a likely cause for the center bias of eye fixations. We investigate the influence of the photographer's center bias on salient object detection, extending our previous work. We show that the centroid locations of salient objects in photographs of Achanta and Liu's data set in fact correlate strongly with a Gaussian model. This is an important insight, because it provides an empirical motivation and justification for the integration of such a center bias in salient object detection algorithms and helps to understand why Gaussian models are so effective. To assess the influence of the center bias on salient object detection, we integrate an explicit Gaussian center bias model into two state-of-the-art salient object detection algorithms. This way, first, we quantify the influence of the Gaussian center bias on pixel- and segment-based salient object detection. Second, we improve the performance in terms of F1 score, Fb score, area under the recall-precision curve, area under the receiver operating characteristic curve, and hit-rate on the well-known data set by Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we exemplarily demonstrate that implicit center biases are partially responsible for the outstanding performance of state-of-the-art algorithms. Last but not least, as a result of debiasing Cheng et al.'s algorithm, we introduce a non-biased salient object detection method, which is of interest for applications in which the image data is not likely to have a photographer's center bias (e.g., image data of surveillance cameras or autonomous robots)

A computational model of visual attention.

Visual attention is a process by which the Human Visual System (HVS) selects most important information from a scene. Visual attention models are computational or mathematical models developed to predict this information. The performance of the state-of-the-art visual attention models is limited in terms of prediction accuracy and computational complexity. In spite of significant amount of active research in this area, modelling visual attention is still an open research challenge. This thesis proposes a novel computational model of visual attention that achieves higher prediction accuracy with low computational complexity. A new bottom-up visual attention model based on in-focus regions is proposed. To develop the model, an image dataset is created by capturing images with in-focus and out-of-focus regions. The Discrete Cosine Transform (DCT) spectrum of these images is investigated qualitatively and quantitatively to discover the key frequency coefficients that correspond to the in-focus regions. The model detects these key coefficients by formulating a novel relation between the in-focus and out-of-focus regions in the frequency domain. These frequency coefficients are used to detect the salient in-focus regions. The simulation results show that this attention model achieves good prediction accuracy with low complexity. The prediction accuracy of the proposed in-focus visual attention model is further improved by incorporating sensitivity of the HVS towards the image centre and the human faces. Moreover, the computational complexity is further reduced by using Integer Cosine Transform (ICT). The model is parameter tuned using the hill climbing approach to optimise the accuracy. The performance has been analysed qualitatively and quantitatively using two large image datasets with eye tracking fixation ground truth. The results show that the model achieves higher prediction accuracy with a lower computational complexity compared to the state-of-the-art visual attention models. The proposed model is useful in predicting human fixations in computationally constrained environments. Mainly it is useful in applications such as perceptual video coding, image quality assessment, object recognition and image segmentation

Image analysis using visual saliency with applications in hazmat sign detection and recognition

Visual saliency is the perceptual process that makes attractive objects stand out from their surroundings in the low-level human visual system. Visual saliency has been modeled as a preprocessing step of the human visual system for selecting the important visual information from a scene. We investigate bottom-up visual saliency using spectral analysis approaches. We present separate and composite model families that generalize existing frequency domain visual saliency models. We propose several frequency domain visual saliency models to generate saliency maps using new spectrum processing methods and an entropy-based saliency map selection approach. A group of saliency map candidates are then obtained by inverse transform. A final saliency map is selected among the candidates by minimizing the entropy of the saliency map candidates. The proposed models based on the separate and composite model families are also extended to various color spaces. We develop an evaluation tool for benchmarking visual saliency models. Experimental results show that the proposed models are more accurate and efficient than most state-of-the-art visual saliency models in predicting eye fixation.^ We use the above visual saliency models to detect the location of hazardous material (hazmat) signs in complex scenes. We develop a hazmat sign location detection and content recognition system using visual saliency. Saliency maps are employed to extract salient regions that are likely to contain hazmat sign candidates and then use a Fourier descriptor based contour matching method to locate the border of hazmat signs in these regions. This visual saliency based approach is able to increase the accuracy of sign location detection, reduce the number of false positive objects, and speed up the overall image analysis process. We also propose a color recognition method to interpret the color inside the detected hazmat sign. Experimental results show that our proposed hazmat sign location detection method is capable of detecting and recognizing projective distorted, blurred, and shaded hazmat signs at various distances.^ In other work we investigate error concealment for scalable video coding (SVC). When video compressed with SVC is transmitted over loss-prone networks, the decompressed video can suffer severe visual degradation across multiple frames. In order to enhance the visual quality, we propose an inter-layer error concealment method using motion vector averaging and slice interleaving to deal with burst packet losses and error propagation. Experimental results show that the proposed error concealment methods outperform two existing methods

Visual Saliency in Video Compression and Transmission

This dissertation explores the concept of visual saliency—a measure of propensity for drawing visual attention—and presents various novel methods for utilization of visual saliencyin video compression and transmission. Specifically, a computationally-efficient method for visual saliency estimation in digital images and videos is developed, which approximates one of the most well-known visual saliency models. In the context of video compression, a saliency-aware video coding method is proposed within a region-of-interest (ROI) video coding paradigm. The proposed video coding method attempts to reduce attention-grabbing coding artifacts and keep viewers’ attention in areas where the quality is highest. The method allows visual saliency to increase in high quality parts of the frame, and allows saliency to reduce in non-ROI parts. Using this approach, the proposed method is able to achieve the same subjective quality as competing state-of-the-art methods at a lower bit rate. In the context of video transmission, a novel saliency-cognizant error concealment method is presented for ROI-based video streaming in which regions with higher visual saliency are protected more heavily than low saliency regions. In the proposed error concealment method, a low-saliency prior is added to the error concealment process as a regularization term, which serves two purposes. First, it provides additional side information for the decoder to identify the correct replacement blocks for concealment. Second, in the event that a perfectly matched block cannot be unambiguously identified, the low-saliency prior reduces viewers’ visual attention on the loss-stricken regions, resulting in higher overall subjective quality. During the course of this research, an eye-tracking dataset for several standard video sequences was created and made publicly available. This dataset can be utilized to test saliency models for video and evaluate various perceptually-motivated algorithms for video processing and video quality assessment

Modeling Bottom-Up Visual Attention Using Dihedral Group D4

Published version. Source at http://dx.doi.org/10.3390/sym8080079 In this paper, first, we briefly describe the dihedral group D4 that serves as the basis for calculating saliency in our proposed model. Second, our saliency model makes two major changes in a latest state-of-the-art model known as group-based asymmetry. First, based on the properties of the dihedral group D4, we simplify the asymmetry calculations associated with the measurement of saliency. This results is an algorithm that reduces the number of calculations by at least half that makes it the fastest among the six best algorithms used in this research article. Second, in order to maximize the information across different chromatic and multi-resolution features, the color image space is de-correlated. We evaluate our algorithm against 10 state-of-the-art saliency models. Our results show that by using optimal parameters for a given dataset, our proposed model can outperform the best saliency algorithm in the literature. However, as the differences among the (few) best saliency models are small, we would like to suggest that our proposed model is among the best and the fastest among the best. Finally, as a part of future work, we suggest that our proposed approach on saliency can be extended to include three-dimensional image data

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms