One of the main objectives of the Mars Exploration Program is to search for evidence of past or current life on the planet. To achieve this, Mars exploration has been focusing on regions on Mars that may have liquid water. A set of critical areas may have seen cycles of ice thawing in the relatively recent past in response to periodic changes in the obliquity of Mars. In our study, we employ the use of convolutional neural networks to identify specific areas of the Martian surface that have a terrain feature referred to as "Brain Coral". This terrain type is known for its resemblance in morphology to "sorted stone circles" found on Earth, which may indicate that it formed due to cycles of freezing and thawing. Brain coral is a term used to describe the texture of a specific type of Martian terrain, which is characterized by stippled, labyrinthine ridges and are found primarily in mid-latitudes. They are often associated with lineated valley fill (LVF) and concentric crater fill (CCF) (e.g., [1] ; [2] ; [3] ). In arctic regions on Earth, "sorted stone circles" form as a result of rock-bearing and water-rich soil layers undergoing cycles of expansion and contraction in response to periodic freezing and thawing of the soil ( [4] ; [5] ; [6] ). These cycles result in rock and soil separation which in turn leads to pattern formation (e.g. [7] ; [8] ; [9] ). However, even though brain coral on Mars and sorted stone circles on Earth have similar appearances, it does not necessarily mean that the same processes formed them. There are multiple young geomorphic features on Mars that suggest thawing occurred in the recent past and may still be ongoing, but there is ongoing controversy about it ( [10] , [11] , [12] , [13] , [14] ). As such, it is crucial to compare existing hypotheses and conduct a thorough examination of this terrain to better understand its formation.

Floods are the most common natural disasters all over the world. The space climate observatory (SCO)-FloodDAM project aims at utilizing the capabilities of new observing strategies in order to better alert, detect and map flood events globally. Leveraging from both aerial- and satellite-based platforms (Sentinel, TerraSar-X, SWOT) as well as in-situ based sensors, the main objective of the project is to develop an automatic system to better prevent flood events and assess their consequences. In this paper, we will demonstrate the strategy deployed for the selected test-sites in France.

This paper focuses on contour tracking, an important problem in computer vision, and specifically on open contours that often directly represent a curvilinear object. Compelling applications are found in the field of bioimage analysis where blood vessels, dendrites, and various other biological structures are tracked over time. General open contour tracking, and biological images in particular, pose major challenges including scene clutter with similar structures (e.g., in the cell), and time varying contour length due to natural growth and shortening phenomena, which have not been adequately answered by earlier approaches based on closed and fixed end-point contours. We propose a model-based estimation algorithm to track open contours of time-varying length, which is robust to neighborhood clutter with similar structures. The method employs a deformable trellis in conjunction with a probabilistic (hidden Markov) model to estimate contour position, deformation, growth and shortening. It generates a maximum a posteriori estimate given observations in the current frame and prior contour information from previous frames. Experimental results on synthetic and real-world data demonstrate the effectiveness and performance gains of the proposed algorithm.

This paper presents an open contour tracking method that employs an arc-emission hidden Markov model (HMM). The algorithm encodes the shape information of the structure in a spatially deformable trellis model that is iteratively modified to account for observations in subsequent frames. As the open contour is determined on the trellis of an HMM, a dynamic programming procedure reduces the computational complexity to linear in the length of the structure (or contour). The method was developed for tracking general curvilinear structures, and tested on subcellular image sequences, where microtubules grow, shrink and undergo lateral motion from frame to frame. Microtubule length changes are modeled by the addition of appropriate transient and absorbing states to the HMM. Our results provide experimental evidence for the proposed algorithm's capability to track non-rigid curvilinear objects in challenging environments in terms of noise and clutter.

Two dimensional hidden markov models (2D-HMMs) provide substantial benefits for many computer vision and image analysis applications. Many fundamental image analysis problems, including segmentation and classification, are target applications for the 2D- HMMs. As opposed to the i.i.d. assumption of the image observations, the naturally existing spatial correlations can be readily modeled by solving the 2D-HMM decoding problem. However, computational complexity of the 2D-HMM decoding grows exponentially with the image size and is known to be NP-hard. In this paper, we present a conditional iterative decoding (CID) algorithm for the approximate decoding of 2D-HMMs. We compare the performance of the CID algorithm to the Turbo-HMM (T-HMM) decoding algorithm and show that CID gives promising results. We demonstrate the proposed algorithm on modeling spatial deformations of human faces in recognizing people across their different facial expressions.

Tracing of curvilinear structures is one of the fundamental tools in the quantitative analysis of biological images, for extracting information about structures such as blood vessels, neurons, microtubules, and similar entities. Due to the limitations in biological sample preparation and fluorescence imaging, typical images in live cell studies exhibit severe noise and considerable clutter. These images are manually analyzed through a laborious and approximate set of quantification tasks. In this paper, we describe a constrained optimization method for extracting curvilinear structures from live cell fluorescence images. We show that the proposed method is largely insensitive to frequent intersections, intensity variations along the curve, and generates successful traces within noisy regions. We demonstrate the results of our approach on live cell microtubule images.

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.