Discriminating terrain traversability stands as a pivotal challenge for autonomous driving in off-road environments. The complexity arises from the diverse and ambiguous nature of off-road conditions, coupled with the specific characteristics of the driving platform. To address this challenge, we introduce a novel self-supervised learning framework for terrain traversability analysis, incorporating a contrastive label disambiguation mechanism. The proposed framework integrates traversability learning with real-time scene reconstruction. By projecting actual driving experience onto the terrain models, weakly labeled training samples with pseudo-labels can be automatically generated. Furthermore, a prototype-based contrastive representation learning method with the aid of a local window-based transformer encoder is designed to learn distinguishable embeddings, facilitating the self-supervised updating of those pseudo labels. Through the iterative interaction between representation learning and pseudo label updating, the inherent ambiguities associated with those pseudo labels are gradually eliminated. This enables the acquisition of fine-grained and platform-specific terrain traversability insights, eliminating the need for any human-provided annotations. Experimental results on the publicly available RELLIS-3D dataset and two self-collected datasets demonstrate the effectiveness of the proposed method.

In this paper, we propose a multi-environment, multi-LiDAR, multi-task dataset to promote the grid map-based navigation capability for autonomous vehicles. The dataset comprises structured and unstructured environmental data captured by different types of LiDAR and contains various challenging scenarios, including moving objects, negative obstacles, steep slopes, cliffs, overhangs, etc. Further, we have devised an innovative method for generating ground truth, facilitating the creation of dense, accurate, and stable grid maps with a minimal requirement for human annotation efforts. A new baseline method and two existing approaches are evaluated on this dataset. Results indicate that existing approaches perform much worse than the proposed baseline. The dataset will be made publicly available at https://github.com/guanglei96/M3-GMN.

For autonomous driving, drivable region detection is one of the most basic and essential tasks. In this paper, a novel LiDAR-based drivable region detection algorithm which could output a complete, accurate and stable result is proposed. To promote the completeness of the detection result, the Bayesian generalized kernel inference and bilateral filtering are utilized to estimate the attribute of those unobserved cells. To ensure the traversability, a region growing operator is performed on the normal vector map which reflects the slope of the terrain, thus closely related to the traversability of the vehicle. To improve the result’s stability, information from multiple frames are fused together in the Kalman Filter framework. Experiments are performed both on public dataset and our own dataset. Experimental results show that the proposed algorithm could run in real-time and outperforms state-of-the-art approaches.

MOTIVATION: For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation. RESULTS: This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30 000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (P < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006. AVAILABILITY AND IMPLEMENTATION: The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The 2017 Intelligent Vehicle Future Challenge of China (IVFC) was held in Changshu between 24th November and 26th November, 2017. As the ninth series of this event, last year's competition has introduced many new features and has attracted 21 teams to join this competition. The HQ3 autonomous vehicle, jointly developed by National University of Defense Technology, Jilin University and Central South University, took part in this competition. This paper mainly describes the key modules of HQ3, including GPS-free localization, environment perception and behavior planning. All of these modules together enable HQ3 to perform well during the competition.

In this work, we present a new multiple channel feature called Deep Compact Channel Feature (DCCF), which generates a compact, discriminative feature representation by a pre-trained deep encoder-decoder. With the combination of DCCF and boosted decision trees, a new object detector is proposed which achieved outstanding performance on standard pedestrian dataset INRIA and Caltech. Furthermore, a large scale and challenging Chinese Traffic Sign Detection benchmark is constructed. DCCF and other related methods are evaluated on this dataset. The dataset and baselines are available online.

In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundary of a gland. Next, we develop a visual feature-based support vector regressor to verify if the detected contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. Our approach can not only detect the existence of the gland, but also can accurately locate it with pixel accuracy. In the experiments, we treat the task of detecting glandular structures as object (gland) detection and segmentation problems respectively. The results indicate that our new technique outperforms state-of-the-art computer vision algorithms in respective fields.

Discovering partially duplicated images such as those of the same scenes, buildings or objects taken from different angles, distances and vantage points can be very useful in applications such as managing large image repositories and image search on the Internet. In this paper, we present a novel technique for partial duplicate image discovery. The new technique, termed tree partition voting min-hash (TmH), first partitions interest points within an image based on their geometric or photometric (appearance) properties using a spatial partition tree data structure and then finds potential partial duplicate images through a traditional partition min-hash (PmH) method [1]. We have developed a k-d tree partition min-hash (kdTmH) and a random projection tree partition min-hash (rpTmH) technique and have also developed a weighted voting algorithm for improving the similarity measure of a pair of hashing sketches. We present experimental results on 3 datasets and show that TmH significantly outperforms PmH in terms of recall and precision performances without increasing complexity and that the new voting algorithms performs better than sketch matching techniques in the literature.

In this paper, we propose a novel method for detecting glandular structures in microscopic images of human tissue. We first transform the image from Cartesian space to polar space and introduce a novel random field model with an efficient inference strategy that uses two simple chain graphs to approximate a circular graph to infer possible boundary of a gland. We then develop a visual feature based support vector regressor (SVR) to verify if the inferred contour corresponds to a true gland. And finally, we combine the outputs of the random field and the regressor to form the GlandVision algorithm for the detection of glandular structures. In the experiments, we treat the task of detecting glandular structures as object (gland) proposal, detection and segmentation problems respectively and show that our new technique outperforms state of the art computer vision algorithms developed for generic objects.

This paper introduces random forest as a computational and data structure paradigm for fusing low-level visual features and high-level semantic concepts for image retrieval. We use visual features to split the tree nodes and use the image labels to supervise the splitting to make images located at the same tree node share similar semantic concepts as well as visual similarities. We exploit such a random forest and define the semantic neighbor set (SNS) of a given image as the union of all images in the leaf nodes that this image falls onto. From SNS we further define the semantic similarity measure (SSM) between two images as the number of trees in which they share the same leaf nodes within a SNS. With SNS and SSM, example-based image retrieval becomes that of first finding the SNS of the querying image and then ranking the images according to the SSMs between the querying image and images in its SNS. We also show that the new technique can be adapted for keyword-based semantic image retrieval. The inherent efficient tree data structure leads to fast solutions. We will present experimental results to show the effectiveness of this new semantic image retrieval technique.

Kernel-based feature combination techniques such as Multiple Kernel Learning use arithmetical operations to linearly combine different kernels. We have observed that the kernel distributions of different features are usually very different. We argue that the similarity distributions amongst the data points for a given dataset should not change with their representation features and propose the concept of relative kernel distribution invariance (RKDI). We have developed a very simple histogram matching based technique to achieve RKDI by transforming the kernels to a canonical distribution. We have performed extensive experiments on various computer vision and machine learning datasets and show that calibrating the kernels to an empirically chosen canonical space before they are combined can always achieve a performance gain over state-of-art methods. As histogram matching is a remarkably simple and robust technique, the new method is universally applicable to kernel-based feature combination.

The aim of semantic segmentation is to assign each pixel a semantic label. Numerous methods for semantic segmentation have been proposed in recent years and most of them chose pixel or super pixel as the processing primitives. However, as the information contained in a pixel or a super pixel is not discriminative enough, the outputs of these algorithms are usually not object consistent. To tackle this problem, we introduce the concept of object-like regions as a new and higher level processing primitive. We first experimentally showed that using object-like regions as processing primitives can boost semantic segmentation accuracy, and then proposed a novel method to produce object-like regions by integrating state-of art low-level segmentation algorithms with typical semantic segmentation algorithms through a novel semantic feature feedback mechanism. We present experimental results on the publicly available image understanding database MSRC21 and show that the new method can achieve state of the art semantic segmentation results with far fewer processing primitives.

This paper introduces the covariance matrix of visually salient image features as a compact and robust descriptor for near duplicate image and video copy detection. We make two novel contributions. We first present a fast method for computing information theoretic based visual saliency maps using a data independent fast transform to replace the conventional data dependent computationally demanding transforms. We then introduce salient covariance (SCOV) - the covariance matrix of various image features within the visually salient regions and use SCOV for near duplicate image and video copy detection. We present experimental results to show that our new fast visual saliency computation technique improves efficiency without compromising performances. We demonstrate that SCOV is a very compact and robust feature for near duplicate image and video copy detection. Compared to popular features such as GIST, SCOV is not only more robust against various manipulations but also can be over 20 times more compact whilst achieving the same or better performances.