Glaciers play a crucial role in regulating hydrological systems. We integrated multisource satellite altimetry data (e.g., ICESat, ICESat-2, Cryosat-2) to derive high-temporal-resolution time series of glacier surface elevation spanning the past two decades across High Mountain Asia surrounding the Tarim Basin (HMA-TB) and analyzed the variability of annual and seasonal glacier elevation changes. We find that glaciers experience great thickening from winter to spring, with the thickening in spring exceeding that in winter. Geographically, this thickening gradually decreases from the west to the east of HMA-TB. Thinning primarily occurs in summer and autumn, with autumn thinning surpassing summer thinning. Geographically, this thinning also gradually decreases from the west to the east. A discernible shift in glacier dynamics is observed in the HMA-TB during the period 2003-2022. The rate of glacier thinning is decelerating in northern HMA-TB, while simultaneously, glaciers in the southern HMA-TB are experiencing a slowdown in thickening. Meanwhile, the western glaciers are undergoing an accelerated rate of thinning, becoming the region with the fastest glacier loss across HMA-TB. Furthermore, an anomalous accumulation of glaciers has been observed in the Tarim Basin in the past five years.

Due to the sophisticated imaging process, an identical scene captured by different cameras could exhibit distinct imaging patterns, introducing distinct proficiency among the super-resolution (SR) models trained on images from different devices. In this paper, we investigate a novel and practical task coded cross-device SR, which strives to adapt a real-world SR model trained on the paired images captured by one camera to low-resolution (LR) images captured by arbitrary target devices. The proposed task is highly challenging due to the absence of paired data from various imaging devices. To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera. DADA employs the Domain-Invariant Attention (DIA) module to establish the basis of target model training even without HR supervision. Furthermore, the dual framework of DADA facilitates an Inter-domain Adversarial Adaptation (InterAA) in one branch for two LR input images from two domains, and an Intra-domain Adversarial Adaptation (IntraAA) in two branches for an LR input image. InterAA and IntraAA together improve the model transferability from the source domain to the target. We empirically conduct experiments under six$\text{Real} \rightarrow \text{Real}$adaptation settings among three different cameras, and achieve superior performance compared with existing state-of-the-art approaches. We also evaluate the proposed DADA to address the adaptation to the video camera, which presents a promising re-search topic to promote the wide applications of real-world super-resolution. Our source code is publicly available at https://github.com/lonelyhopeIDADA.

Referring image segmentation aims at segmenting out the object or stuff referred to by a natural language expression. The challenge of this task lies in the requirement of understanding both vision and language. The linguistic structure of a referring expression can provide an intuitive and explainable layout for reasoning over visual and linguistic concepts. In this paper, we propose a structured attention network (SANet) to explore the multimodal reasoning over the dependency tree parsed from the referring expression. Specifically, SANet implements the multimodal reasoning using an attentional multimodal tree-structure recurrent module (AMTreeGRU) in a bottom-up manner. In addition, for spatial detail improvement, SANet further incorporates the semantics-guided low-level features into high-level ones using the proposed attentional skip connection module. Extensive experiments on four public benchmark datasets demonstrate the superiority of our proposed SANet with more explainable visualization examples.

In recent years, there has been a rapid increase in the size and number of medical image collections. Thus, the development of appropriate methods for medical information retrieval is especially important. In a large collection of spine X-ray images, maintained by the National Library of Medicine, vertebral boundary shape has been determined to be relevant to pathology of interest. This paper presents an innovative partial shape matching (PSM) technique using dynamic programming (DP) for the retrieval of spine X-ray images. The improved version of this technique called corner-guided DP is introduced. It uses nine landmark boundary points for DP search and improves matching speed by approximately 10 times compared to traditional DP. The retrieval accuracy and processing speed of the retrieval system based on the new corner-guided PSM method are evaluated and included in this paper.

Fast retrieval of images from large databases especially from large medical image databases is of great interest to researchers. We have developed shape-based image retrieval system for spine X-ray images that applies Dynamic Programming (DP) in Partial Shape Matching (PSM) techniques to vertebral boundary data. As we enable Internet access to this system with an aim to enable CBIR access to the entire NHANES II spine X-ray image collection, retrieval efficiency has become critical. In this paper, we present enhancements to our existing sequential retrieval model to provide faster partialshape- based vertebrae retrieval. Based on the characteristics of vertebral osteophyte pathology, anterior superior and anterior inferior parts are the areas with the most interest to the users and are indexed for each shape. Pair-wise distances between indexed parts are pre-calculated and used by agglomerative clustering algorithm to index the whole database. To increase the retrieval speed, PSM with DP is therefore conducted only on a small selected set of shapes by the pre-indexing retrieval.

Relevance feedback (RF) has been an active research area in content-based image retrieval (CBIR). RF intends to bridge the gap between the low-level image features and the high-level human visual perception by analyzing and employing the feedback information provided by the user. This gap becomes more evident and important in medical image retrieval due to the two distinct facts with regard to medical images: (1) subtle differences between images, even between pathological and non-pathological images; (2) subjective and different diagnosis even among experts. This paper describes a novel linear weight-updating approach for RF applying to spine X-ray image retrieval. The algorithm utilizes both positive and negative examples to gain feedback from the user. Experimental results show that the proposed approach can substantially improve the retrieval performance to better satisfy the individual user's preferences.

The osteophyte shows only on some particular locations on the vertebra. This indicates that other locations on the vertebra shape contain that are not of interest hinder the spine X-ray image retrieval relevance precision, which motivates our research in partial shape matching (PSM). This paper presents PSM methods for matching shapes with variable number of points and with different data point distributions. Dynamic programming (DP) is proposed for matching partial shapes by allowing merging of the data points in the process of PSM. DP is implemented based on two shape representation methods: line segments and multiple open triangles. The performance evaluation, which is based on human relevance judgements of these two shape representations in PSM is also presented.

In this research, an imaging system was built to work with a newly developed electronic device to help people produce sounds correctly. The system consists of two parts, the internal tongue contact pattern data collection and the external lip shape information analysis. The tongue position information was gathered using the palatometer, an innovative tongue contact pattern-tracking device invented by Dr. Samuel Fletcher. The lip shape information was collected by processing images taken from people articulating different sounds. We developed an efficient color image segmentation technique to extract lip contour points and form a closed curve for shape analysis. The geometry invariant turn function vs. normalized length was then calculated for the lip shape for each sound and compared against the turn function of the lips in a resting position to quantify their variations from this reference. Both internal (vocal tract) and external (visible lip shape) information was collected for each of the speech sounds. The lip shape information extracted from the images was then correlated with tongue position information. The test results showed that this imaging system can be used to quantify the lip shape information and its relations with the tongue position and is a potentially useful tool for speech pathology research.

Vertebra shape can effectively describe various pathologies found in spine X-ray images. There are some critical regions on the shape contour which help determine whether the shape is pathologic or normal. We selected a subset of 250 segmented vertebra boundaries for study from a collection of 17,000 digitized X-rays of cervical and lumbar spine taken as a part of the second National Health and Nutrition Examination Survey (NHANES II). A board certified expert radiologist marked nine morphometric landmark points on the contour of these cervical and lumbar images. Image indexing could mimic the model used by the radiologists to mark the images, e.g. 6-, 9-, or 10-point, thereby improve the query and retrieval of vertebra shapes from the image database. In this paper, we present a technique to automatically select nine points from the boundary contour. The comparison between two 9-point models using the L/sub 2/ distance and retrieval rank results derived respectively from the 9-point model marked by the expert and the 9-point model selected with our algorithm provides a good measure of how well the two models match.