ImageSpirit: Verbal Guided Image Parsing

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit

Using encoder-decoder architecture for material segmentation based on beam profile analysis

Abstract. Recognition and segmentation of materials has proven to be a challenging problem because of the wide divergence in appearance within and between categories. Many recent material segmentation approaches treat materials as yet another set of labels like objects. However, materials are basically different from objects as they have no basic shape or defined spatial extent. Our approach roughly ignores this and can primarily take advantage of limited implicit context (local appearance) as it seems during training, because our training images that almost do not have a global image context; such as (I) where the used materials have no inherent shape or defined spatial extent like apple, orange and potato approximately have the same spherical shape; (II) besides, images where taken under a black background, which roughly removes the spatial features of the materials. We introduce a new materials segmentation dataset, which was taken with a Beam Profile Analysis sensing device. The dataset contains 10 material categories, and it has image pair samples consisting of grayscale images with and without the laser spots (grayscale and laser images) in addition to annotated segmented images. To the best of our knowledge, this is the first material segmentation dataset for Beam Profile Analysis images. As a second step, we proposed a deep learning approach to perform material segmentation on our dataset; our proposed CNNs is an encoder-decoder model, which is based on the DeeplabV3+ model. Our main goal is to obtain segmented material maps and discover how the laser spots contribute to the segmentation results; therefore, we perform a comparative analysis across different types of architectures to observe how the laser spots contribute to the whole segmentation. We built our experiments on three main types of models that use a different type of input; for each model, we implemented various types of backbone architectures. Our experiments results show that the laser spots have an efficient contribution on the segmentation results. GrayLaser model achieves a significant accuracy improvement compared to other models, where the fine-tuned architecture of this model has reached an accuracy of 94% over MIoU metric, and one trained from the scratch has reached an accuracy of 62% over MIoU

Object-based attention mechanism for color calibration of UAV remote sensing images in precision agriculture.

Color calibration is a critical step for unmanned aerial vehicle (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g. plant health, disease, and pest stresses. In UAV remote sensing, the exemplar-based color transfer is popularly used for color calibration, where the automatic search for the semantic correspondences is the key to ensuring the color transfer accuracy. However, the existing attention mechanisms encounter difficulties in building the precise semantic correspondences between the reference image and the target one, in which the normalized cross correlation is often computed for feature reassembling. As a result, the color transfer accuracy is inevitably decreased by the disturbance from the semantically unrelated pixels, leading to semantic mismatch due to the absence of semantic correspondences. In this article, we proposed an unsupervised object-based attention mechanism (OBAM) to suppress the disturbance of the semantically unrelated pixels, along with a further introduced weight-adjusted Adaptive Instance Normalization (AdaIN) (WAA) method to tackle the challenges caused by the absence of semantic correspondences. By embedding the proposed modules into a photorealistic style transfer method with progressive stylization, the color transfer accuracy can be improved while better preserving the structural details. We evaluated our approach on the UAV data of different crop types including rice, beans, and cotton. Extensive experiments demonstrate that our proposed method outperforms several state-of-the-art methods. As our approach requires no annotated labels, it can be easily embedded into the off-the-shelf color transfer approaches. Relevant codes and configurations will be available at https://github.com/huanghsheng/object-based-attention-mechanis

Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.Comment: 29 pages; 13 figures; 8 table

Dynamic Gaussian Splatting from Markerless Motion Capture can Reconstruct Infants Movements

Easy access to precise 3D tracking of movement could benefit many aspects of rehabilitation. A challenge to achieving this goal is that while there are many datasets and pretrained algorithms for able-bodied adults, algorithms trained on these datasets often fail to generalize to clinical populations including people with disabilities, infants, and neonates. Reliable movement analysis of infants and neonates is important as spontaneous movement behavior is an important indicator of neurological function and neurodevelopmental disability, which can help guide early interventions. We explored the application of dynamic Gaussian splatting to sparse markerless motion capture (MMC) data. Our approach leverages semantic segmentation masks to focus on the infant, significantly improving the initialization of the scene. Our results demonstrate the potential of this method in rendering novel views of scenes and tracking infant movements. This work paves the way for advanced movement analysis tools that can be applied to diverse clinical populations, with a particular emphasis on early detection in infants