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.

The rapidly growing exploitation and utilization of marine resources by humans has sparked considerable interest in underwater object detection tasks. Targets captured in underwater environments differ significantly from those captured in general images owing to various factors, such as water turbidity, complex background conditions, and lighting variations. These adverse factors pose a host of challenges, such as high intensity noise, texture distortion, uneven illumination, low contrast, and limited visibility in underwater images. To address the specific difficulties encountered in underwater environments, numerous underwater object detection methods have been developed in recent years in response to these challenges. Furthermore, there has been a significant effort in constructing diverse and comprehensive underwater datasets to facilitate the development and evaluation of these methods. This paper outlines 14 traditional methods used in underwater object detection based on three aspects that rely on handmade features. Thirty-four more advanced technologies based on deep learning were presented from eight aspects. Moreover, this paper conducts a comprehensive study of seven representative datasets used in underwater object detection missions. Subsequently, the challenges encountered in current underwater object detection tasks were analyzed from five directions. Based on the findings, potential research directions are expected to promote further progress in this field and beyond.

In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR-HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition.