Predictive Uncertainty Estimation for Camouflaged Object Detection.

Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed in background. The strong enquote center bias of the training dataset leads to models of poor generalization ability as the models learn to find camouflaged objects around image center, which we define as enquote model bias. Further, due to the similar appearance of camouflaged object and its surroundings, it is difficult to label the accurate scope of the camouflaged object, especially along object boundaries, which we term as enquote data bias.

Designing Two Secure Keyed Hash Functions Based on Sponge Construction and the Chaotic Neural Network.

In this paper, we propose, implement, and analyze the structures of two keyed hash functions using the Chaotic Neural Network (CNN). These structures are based on Sponge construction, and they produce two variants of hash value lengths, i.e.

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors.

In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain.

NIQSV+: A No-Reference Synthesized View Quality Assessment Metric.

Benefiting from multi-view video plus depth and depth-image-based-rendering technologies, only limited views of a real 3-D scene need to be captured, compressed, and transmitted. However, the quality assessment of synthesized views is very challenging, since some new types of distortions, which are inherently different from the texture coding errors, are inevitably produced by view synthesis and depth map compression, and the corresponding original views (reference views) are usually not available. Thus the full-reference quality metrics cannot be used for synthesized views.

Joint source-channel coding: secured and progressive transmission of compressed medical images on the Internet.

The joint source-channel coding system proposed in this paper has two aims: lossless compression with a progressive mode and the integrity of medical data, which takes into account the priorities of the image and the properties of a network with no guaranteed quality of service. In this context, the use of scalable coding, locally adapted resolution (LAR) and a discrete and exact Radon transform, known as the Mojette transform, meets this twofold requirement. In this paper, details of this joint coding implementation are provided as well as a performance evaluation with respect to the reference CALIC coding and to unequal error protection using Reed-Solomon codes.