The objective of image compression is to reduce irrelevance and redundancy of the image data to be able to store or transmit data in an efficient manner by minimizing the number of bits required to represent an image accurately. JPEG is capable of achieving an image compression ratio of 10:1 with little perceptible loss in image quality using standard metrics, and has become the most widely used standard image compression in the world since its release. Traditionally, compression techniques have relied on linear transforms to approximate 2-D signals (images), and the omission of specific constituent vectors has been mostly arbitrary. These techniques can save incredible amounts of memory while retaining image integrity. Recently techniques have been developed that use neural networks to approximate these signals. These networks offer the advantage of decorrelating image data to find a series of vectors to represent an image that is smaller than traditional techniques by estimating gradient descent, thus finding the minimum number of bits required to represent an image. Expansion to the development of these architectures is happening rapidly through informed design drawing upon other fields that have recently seen increased focus such as computer vision and image analysis applications. A novel efficient neural network is proposed in this work to compress infrared images at state of the art levels while preserving overall image quality to handle the demands spanning from the daily commute to combat environments.
As one of the classic fields within the area of computer vision, image classification and segmentation solutions as topics have expanded exponentially in terms of accuracy and ease of use. On Mars, the atmospheric and surface conditions can lead to the sudden onset of a dust storm, or a more common dust devil, causing a multitude of issues for both equipment and crew. The ability to identify and locate area which should be avoided due to these storms is necessary for mission safety. Many current techniques are not practical due to being hefty and computationally expensive for specific tasks that require the ability for swift deployability onto systems with more stringent constraints. This paper proposes a novel approach to the problem of segmentation by marrying an efficient yet powerful Vision Transformer based model with traditional signal processing techniques to ensure peak performance. With the National Aeronautics and Space Administration (NASA) looking to land a team on Mars, this paper takes on the real time hurdle of classifying and segmenting dust storms within remote satellite equatorial photos, using a model designed to be integrated on any and all future systems, increasing overall mission success.
The detection and recognition of targets within imagery and video analysis is vital for military and commercial applications. The development of infrared sensor devices for tactical aviation systems imagery has increased the performance of target detection. Due to the advancements of infrared sensors capabilities, their use for field operations such as visual operations (visops) or reconnaissance missions that take place in a variety of operational environments have become paramount. Many techniques implemented stretch back to 1970, but were limited due to computational power. The AI industry has recently been able to bridge the gap between traditional signal processing tools and machine learning. Current state of the art target detection and recognition algorithms are too bloated to be applied for on ground or aerial mission reconnaissance. Therefore, this paper proposes Edge IR Vision Transformer (EIR-ViT), a novel algorithm for automatic target detection utilizing infrared images that is lightweight and operates on the edge for easier deployability.
As one of the classic fields of computer vision, image classification is a topic that has expanded exponentially in terms of usability and accuracy in recent years. With the rapid progression of deep learning, as well as the introduction and advancement of techniques such as convolutional neural networks and vision transformers, image classification has been elevated to levels only theoretical until modern times. This paper presents an improved method of object classification using a combination of vision transformers and multilayer convolutional neural networks with specific application to underwater environments. In comparison to previous underwater object classification algorithms, the proposed network classifies images with higher accuracy, shorter training iterations, and deployable parameters.
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