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The large amount of data collected by LiDAR sensors brings the issue of LiDAR point cloud compression (PCC). Previous works on LiDAR PCC have used range image representations and followed the predictive coding paradigm to create a basic prototype of a coding framework. However, their prediction methods give an inaccurate result due to the negligence of invalid pixels in range images and the omission of future frames in the time step. Moreover, their handcrafted design of residual coding methods could not fully exploit spatial redundancy. To remedy this, we propose a coding framework BIRD-PCC. Our prediction module is aware of the coordinates of invalid pixels in range images and takes a bidirectional scheme. Also, we introduce a deep-learned residual coding module that can further exploit spatial redundancy within a residual frame. Experiments conducted on SemanticKITTI and KITTI-360 datasets show that BIRD-PCC outperforms other methods in most bitrate conditions and generalizes well to unseen environments., Comment: Accepted to ICASSP 2023

We introduce a query-driven approach (qMIL) to multi-instance learning where the queries aim to uncover the class labels embodied in a given bag of instances. Specifically, it solves a multi-instance multi-label learning (MIML) problem with a more challenging setting than the conventional one. Each MIML bag in our formulation is annotated only with a binary label indicating whether the bag contains the instance of a certain class and the query is specified by the word2vec of a class label/name. To learn a deep-net model for qMIL, we construct a network component that achieves a generalized compatibility measure for query-visual co-embedding and yields proper instance attentions to the given query. The bag representation is then formed as the attention-weighted sum of the instances' weights, and passed to the classification layer at the end of the network. In addition, the qMIL formulation is flexible for extending the network to classify unseen class labels, leading to a new technique to solve the zero-shot MIML task through an iterative querying process. Experimental results on action classification over video clips and three MIML datasets from MNIST, CIFAR10 and Scene are provided to demonstrate the effectiveness of our method.

One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo ground truth corrected with motion cue for video object segmentation training. Our method conducts semantic segmentation using instance segmentation networks and, then, selects the segmented object of interest as the pseudo ground truth based on the motion information. Afterwards, the pseudo ground truth is exploited to finetune the pretrained objectness network to facilitate object segmentation in the remaining frames of the video. We show that the pseudo ground truth could effectively improve the segmentation performance. This straightforward unsupervised video object segmentation method is more efficient than existing methods. Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets. And the category-agnostic pseudo ground truth has great potential to extend to multiple arbitrary object tracking.

Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a given video clip. In this paper, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. DOA models spatial-temporal consistency in video sequences by capturing background dependencies from adjacent frames. Instance proposals are generated by the instance segmentation network for each frame and then selected by motion information as hard negatives if they exist and positives. To adopt high-quality hard negatives, the block matching algorithm is then applied to preceding frames to track the associated hard negatives. General negatives are also introduced in case that there are no hard negatives in the sequence and experiments demonstrate both kinds of negatives (distractors) are complementary. Finally, we conduct DOA using the positive, negative, and hard negative masks to update the foreground/background segmentation. The proposed approach achieves state-of-the-art results on two benchmark datasets, DAVIS 2016 and FBMS-59 datasets., 11 pages, 6 figures, 4 tables, conference

Body weight-transfer plays an important role in many exercises. The correlation of the body posture, movement, and weight-transfer will mutually affect the trainee to do well in performances such as Tai-Chi exercise. According to the traditional way of learning Tai-Chi, we proposed Stillness Moves, a physical training system for Tai-Chi, which captures and records users' skeleton movement and weight-transfer information for offering real-time and summary visual feedback. Based on above, we provide a gradual learning program in physical training, which combines body movement and weight-transfer learning. We evaluated our system and compared the performance without and with weight-transfer guidance in the user study. The result demonstrated that weight-transfer guidance is beneficial for trainee learning the Tai-Chi moves. For difficult moves, the trainee should learn the weight-transfer first, then, learning the body movement.

Multimedia capturing and display devices of different resolutions and aspect ratios can be easily connected by networks and, thus, there is a great need to develop techniques that facilitate flexible image/video format conversion and content adaptation among these heterogeneous terminals. Quality degradation due to downsampling, up-sampling, coding/decoding, and some content adaptation mechanism (say, image mosaicking) in the transmission process is inevitable. It is desirable that multimedia contents can be easily captured, displayed, and seamlessly composed. Challenges and techniques to achieve this goal are reviewed first. Then, two specific topics, i.e., image/video mosaicking and super resolution (SR) conversion, are highlighted. As compared with previous work developed for these problems, the challenge under the current context is to strike a balance between low computational complexity and high quality of resultant image/video. Several new developments along this line are discussed

Images/videos captured from optical devices are usually degraded by turbid media such as haze, smoke, fog, rain and snow. Haze is the most common problem in outdoor scenes because of the atmosphere conditions. This paper proposes a novel single image-based dehazing framework to remove haze artifacts from images, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze optical model, we propose to estimate atmospheric light via haze density analysis. We can then estimate transmission map, followed by refining it via the bilateral filter. As a result, high-quality haze-free images can be recovered with lower computational complexity compared with the state-of-the-art approach based on patch-based dark channel prior.

Breathwalk is a science of combining specific patterns of footsteps synchronized with the breathing. In this study, we developed a multimedia-assisted Breathwalk-aware system which detects user's walking and breathing conditions and provides appropriate multimedia guidance on the smartphone. Through the mobile device, the system enhances user's awareness of walking and breathing behaviors. As an example application in slow technology, the system could help meditator beginners learn “walking meditation,” a type of meditation which aims to be as slow as possible in taking pace, to synchronize footstep with breathing, and to land every footstep with toes first. In the pilot study, we developed a walking-aware system and evaluated whether multimedia-assisted mechanism is capable of enhancing beginner's walking awareness while walking meditation. Experimental results show that it could effectively assist beginners in slowing down the walking speed and decreasing incorrect footsteps. In the second experiment, we evaluated the Breathwalk-aware system to find a better feedback mechanism for learning the techniques of Breathwalk while walking meditation. The experimental results show that the visual-auditory mechanism is a better multimedia-assisted mechanism while walking meditation than visual mechanism and auditory mechanism.

The coming ubiquity of digital media content is driving the need of a solution for improving the interaction between the people and media. In this work, we proposed a novel interaction technique, QPalm, which allows the user to control the media via a list menu shown on a distant display by drawing circles in the air with one hand. To manipulate a list menu remotely, QPalm includes two basic functions, browse and choosing, realized by recognizing the userpsilas palm performing circular and push motions in the air. The circular motion provides fluidity in scrolling a menu up and down, while push motion is intuitive when the user decided to choose an item during a circular motion. Based on this design, we develop a vision system based on a stereo camera to track the userpsilas palm without interfering by intruders behind or next to the operating user. For more specifically, the contribution of the work includes: (1) an intuitive interaction technique, QPalm, for remote control with list menu, and (2) a palm tracking algorithm to support QPalm based on merely depth and motion information of images for a practical consideration.

A content adaptive technique is proposed to upsample an image to an output image of higher resolution in this work. The proposed technique is a block-based processing algorithm that offers the flexibility in choosing the most suitable up-sampling method for a particular block type. Block classification is first conducted in the DCT domain to categorized each image block into several types: smooth areas, textures, edges and others. For the plain background and smooth surfaces, simple patches are used to enlarge the image size without degrading the resultant visual quality. The unsharp masking method is applied to the textured region to preserve high frequency components. Since human eyes are more sensitive to edges, we adopt a more sophisticated technique to process edge blocks. That is, they are approximated by a facet model so that the image data at subpixel positions can be generated accordingly. A post-processing technique such as ID directional unsharp masking can be used to enhance edge sharpness furthermore. Experimental results are given to demonstrate the efficiency of the proposed technique.