Hybrid image representation methods for automatic image annotation: a survey

In most automatic image annotation systems, images are represented with low level features using either global methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is beneficial in annotating images. In this paper, we provide a survey on automatic image annotation techniques according to one aspect: feature extraction, and, in order to complement existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation

Based on Perceptron Object Classification Algorithms for Processing of Agricultural Field Images

Neural network algorithms of object classification are considered in the paper applying to disease area recognition of agricultural field images. The images are presented as reduced normalized histograms. The classification is carried out for RGB-and HSV-space by using of a multilayer perceptron

A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture

Drone images from an experimental field cropped with sugar beet with a high diffusion of weeds taken from different flying altitudes were used to develop and test a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing analysis, digital transformation by an image embedder, and evaluation by supervised learning. Specifically, six of the most common machine learning algorithms were applied (i.e., logistic regression, k-nearest neighbors, decision tree, random forest, neural network, and support-vector machine). The proposed method was able to precisely recognize crops and weeds throughout a wide cultivation field, training from single partial images. The information has been designed to be easily integrated into autonomous weed management systems with the aim of reducing the use of water, nutrients, and herbicides for precision agriculture

Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy

The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges) were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1) was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Review on Human Re-identification with Multiple Cameras

Human re-identification is the core task in most surveillance systems and it is aimed at matching human pairs from different non-overlapping cameras. There are several challenging issues that need to be overcome to achieve reidentification, such as overcoming the variations in viewpoint, pose, image resolution, illumination and occlusion. In this study, we review existing works in human re-identification task. Advantages and limitations of recent works are discussed. At the end, this paper suggests some future research directions for human re-identification

Semantic Modeling of Outdoor Scenes for the Creation of Virtual Environments and Simulations

Efforts from both academia and industry have adopted photogrammetric techniques to generate visually compelling 3D models for the creation of virtual environments and simulations. However, such generated meshes do not contain semantic information for distinguishing between objects. To allow both user- and system-level interaction with the meshes, and enhance the visual acuity of the scene, classifying the generated point clouds and associated meshes is a necessary step. This paper presents a point cloud/mesh classification and segmentation framework. The proposed framework provides a novel way of extracting object information – i.e., individual tree locations and related features while considering the data quality issues presented in a photogrammetric-generated point cloud. A case study has been conducted using data that were collected at the University of Southern California to evaluate the proposed framework

Fuzzy classification improvement by a pre-perceptual labelled segmentation algorithm

The goal of this paper is to present how two different image processing approaches can be enhanced by merging both methodologies. We will see how the results of a perceptual labelled segmentation methodology [7] can be improved by applying a fuzzy classification algorithm [2] based on a fuzzy outranking methodology [9] as a postprocessing algorithm, and viceversa. A comparison of the individual algorithms with the combination of both algorithms will be presented in order to demonstrate the improvement. Color Bone Marrow (1) images will be used. The objective is to detect White Blood Cells. The detection of white blood cells in bone marrow microscopic images presents big difficulties because of the great variance in their characteristics and also because of staining and illumination inconsistences. On the other hand, the maturity classes of white blood cells actually represents a continuum; cells frequently overlap each other, and there is a fairly wide variation in size and shape of nucleus and cytoplasm regions within given cell classes