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Image analysis
Image analysis
Image analysis
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Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques.[1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face.

Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications — including medicine, security, and remote sensing — human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models.

Digital

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Digital Image Analysis or Computer Image Analysis is when a computer or electrical device automatically studies an image to obtain useful information from it. Note that the device is often a computer but may also be an electrical circuit, a digital camera or a mobile phone. It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing. This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab, originally as a branch of artificial intelligence and robotics.

It is the quantitative or qualitative characterization of two-dimensional (2D) or three-dimensional (3D) digital images. 2D images are, for example, to be analyzed in computer vision, and 3D images in medical imaging. The field was established in the 1950s—1970s, for example with pioneering contributions by Azriel Rosenfeld, Herbert Freeman, Jack E. Bresenham, or King-Sun Fu.

Techniques

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There are many different techniques used in automatically analysing images. Each technique may be useful for a small range of tasks, however there still aren't any known methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human's image analysing capabilities. Examples of image analysis techniques in different fields include:

Applications

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The applications of digital image analysis are continuously expanding through all areas of science and industry, including:


Object-based

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Image segmentation during the object base image analysis

Object-based image analysis (OBIA) involves two typical processes, segmentation and classification. Segmentation helps to group pixels into homogeneous objects. The objects typically correspond to individual features of interest, although over-segmentation or under-segmentation is very likely. Classification then can be performed at object levels, using various statistics of the objects as features in the classifier. Statistics can include geometry, context and texture of image objects. Over-segmentation is often preferred over under-segmentation when classifying high-resolution images.[5]

Object-based image analysis has been applied in many fields, such as cell biology, medicine, earth sciences, and remote sensing. For example, it can detect changes of cellular shapes in the process of cell differentiation.;[6] it has also been widely used in the mapping community to generate land cover.[5][7]

When applied to earth images, OBIA is known as geographic object-based image analysis (GEOBIA), defined as "a sub-discipline of geoinformation science devoted to (...) partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale".[8][7] The international GEOBIA conference has been held biannually since 2006.[9]

OBIA techniques are implemented in software such as eCognition or the Orfeo toolbox.

See also

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Image analysis

View on Grokipedia
from Grokipedia
Image analysis is the process of extracting quantitative numerical data from digital through a combination of image processing techniques and operations, enabling the identification, quantification, and interpretation of features within visual data. This field distinguishes itself from general image processing, which focuses on enhancing or transforming for better visibility or transmission, by emphasizing the derivation of measurable attributes such as area, , intensity, or spatial relationships. At its core, image analysis automates the recognition of patterns or structures in , often involving automated apparatus and methods to convert visual content into structured, analyzable information. The workflow of image analysis typically begins with image acquisition and preprocessing to correct imperfections like or uneven illumination, using filters such as Gaussian or convolution. Subsequent steps include enhancement to highlight relevant details, such as via Sobel or Canny operators, and segmentation through thresholding or watershed algorithms to isolate objects of interest. Feature extraction then quantifies properties like size, orientation, or texture, often culminating in statistical analysis for broader insights. These processes ensure and reduce human bias, with original images preserved and all transformations documented for validation. Applications of image analysis span diverse domains, including for quantifying cellular markers in pathology slides, such as nuclear staining in assays for cancer diagnostics. In and , it facilitates measurements of microstructures like lipid distributions or tissue components, aiding research in cellular dynamics. Broader uses include document analysis for text and graphic recognition, for , and industrial through automated defect detection. Recent advancements integrate models, enhancing accuracy in tasks like semantic segmentation and across these fields.

Fundamentals

Definition and Scope

Image analysis is the process of extracting meaningful information from images, encompassing both quantitative measurements, such as object size and shape, and qualitative assessments, like object identification and . This field treats images as fundamental data sources from which descriptive or analytical insights are derived, often involving the identification of features or structures within visual content. At its core, image analysis operates on pixels as the basic units of digital images, represented as matrices of intensity values, to achieve goals such as detection, recognition, and interpretation of visual elements. The scope of image analysis extends to both two-dimensional (2D) and three-dimensional (3D) representations, as well as static images and dynamic sequences like video frames, enabling the processing of diverse visual data types. It bridges biological human —where mechanisms like those in the visual cortex process edges and patterns—and computational methods that mimic or extend these capabilities through algorithms. Pioneered by early experiments in and neural processing, the field has evolved to support interdisciplinary applications across , , and , such as morphological analysis in or structural evaluation in . Unlike image processing, which primarily focuses on enhancement and manipulation of images for improved quality, image analysis emphasizes , while it differs from computer vision by concentrating on targeted feature derivation rather than full scene understanding. Examples illustrate the range of complexity in image analysis tasks: simple applications include barcode scanning, where algorithms detect and decode linear patterns from captured images, whereas more intricate ones involve scene interpretation for autonomous vehicles, requiring the extraction of multiple object attributes from real-time visual inputs. This foundational approach ensures that image analysis serves as a versatile tool for deriving actionable insights from visual data across varied domains.

Historical Development

The roots of image analysis trace back to the early , when manual techniques dominated in fields like and . In the 1920s and 1930s, researchers in began systematically interpreting visual data from biological samples, using qualitative assessments to identify cellular structures and patterns, laying groundwork for quantitative methods later. Similarly, photographic analysis in astronomy and forensics involved hand-drawn measurements and visual comparisons, as seen in early efforts during . These analog approaches, though labor-intensive, established foundational concepts of feature identification and interpretation that would inform computational advancements. The field emerged as a distinct discipline in the 1950s with the advent of digital computers, enabling initial automated processing. A pivotal milestone was Lawrence Roberts' 1963 PhD thesis at MIT, "Machine Perception of Three-Dimensional Solids," which introduced block-world scene analysis using edge detection on digitized images, marking one of the earliest attempts at computational 3D interpretation from 2D visuals. This work, supported by military funding through ARPA (precursor to DARPA), highlighted the potential for computers in visual perception. In parallel, NASA's Jet Propulsion Laboratory (JPL) developed digital enhancement techniques in the mid-1960s for lunar probe images, such as those from Ranger 7 in 1964, to improve clarity for scientific analysis amid transmission noise. These efforts, driven by space exploration needs, accelerated the shift from analog to digital methods. Azriel Rosenfeld's 1969 book, Picture Processing by Computer, formalized image analysis as an academic field, covering encoding, approximation, and recognition of pictorial data, and is widely regarded as the first comprehensive text on the subject. The 1970s saw advancements in , with techniques like the (patented in 1962 but applied broadly then) for line detection, and the formation of the International Association for Pattern Recognition (IAPR) in 1976 to foster global collaboration. Military and funding continued to propel research, funding projects at institutions like MIT and Stanford for scene understanding. By the 1980s, integration with artificial intelligence emerged, incorporating expert systems and early neural networks for tasks like , as exemplified by Fukushima's model in 1980. The 1990s marked the rise of standardized , facilitating broader adoption; the standard, evolving from 1980s efforts, became integral for medical image interchange by the decade's end, enabling consistent analysis across systems. This transition from analog manual methods to computational frameworks in the late established the core principles—such as segmentation and feature extraction—that underpin modern techniques, including the post-2010 surge in integrations.

Digital Foundations

Image Acquisition and Digitization

Image acquisition is the initial step in image analysis, where real-world visual data is captured using specialized hardware to convert continuous optical signals into a form suitable for digital processing. Common methods include digital cameras employing (CCD) or (CMOS) sensors, which detect intensity across an array of photosites to form a two-dimensional image. CCD sensors transfer charge sequentially for high-quality, low-noise capture, while sensors enable faster readout and lower power consumption through on-chip amplification, making them prevalent in modern consumer and industrial applications. Scanners, such as flatbed or drum types, acquire images by mechanically moving a source and over reflective or transmissive media, ideal for digitizing documents or artwork with uniform illumination. In medical contexts, magnetic resonance imaging (MRI) and computed tomography (CT) systems acquire volumetric data; MRI uses radiofrequency pulses and magnetic fields to map tissue properties without , whereas CT employs X-rays and rotating detectors to reconstruct cross-sectional slices based on density differences. Key factors influencing acquisition quality include , which defines the smallest discernible detail (measured in pixels per unit length), bit depth, representing the number of bits per for intensity encoding, and inherent from sensor electronics or environmental interference. Higher resolution enhances detail fidelity but increases data volume and potential for , while greater bit depth, such as 12- or 16-bit, preserves subtle gradations in high-dynamic-range scenes compared to standard 8-bit. , manifesting as random variations, arises during detection or readout and degrades , particularly in low-light conditions. Digitization follows acquisition, transforming the into discrete digital values through sampling and quantization. Sampling involves measuring the continuous intensity signal at regular spatial intervals, governed by the Nyquist-Shannon sampling , which requires the sampling frequency fsf_s to be at least twice the maximum signal frequency fmax⁡f_{\max} (i.e., fs≥2fmax⁡f_s \geq 2f_{\max}) to prevent artifacts where high frequencies masquerade as lower ones. Quantization then maps these samples to finite intensity levels; for instance, 8-bit quantization divides the into 256 discrete steps (0 to 255), balancing storage efficiency with perceptual fidelity, though it can introduce quantization error in smooth gradients. Challenges in acquisition and include optical and electronic artifacts such as geometric from lens imperfections or scanner misalignment, and illumination variance causing uneven brightness across the field of view. These issues can propagate errors into . To manage the resulting volume, many camera systems apply compression such as the format, which uses discrete cosine transform-based encoding during or immediately after acquisition to reduce while maintaining visual quality, achieving typical 10:1 compression ratios with minimal perceptible loss. The resulting , often represented as a matrix of quantized values, serves as the foundation for subsequent processing steps like .

Image Representation and Data Structures

In , images are fundamentally represented as discrete data structures derived from continuous functions. A continuous image can be modeled as a two-dimensional function f(x,y)f(x, y), where xx and yy denote spatial coordinates in the plane, and the f(x,y)f(x, y) represents the intensity at that point. Upon , this function is sampled and quantized to form a finite 2D or matrix of values, typically of size M×NM \times N, where each element corresponds to the intensity of a . For color images in the RGB color space, representation extends to a 3D array of dimensions M×N×3M \times N \times 3, where the third dimension captures the red (R), green (G), and blue (B) component intensities at each pixel. This structure treats the image as three interleaved monochrome planes, enabling additive color synthesis. Multispectral images, which capture data across multiple wavelength bands beyond visible light, are similarly stored as 3D arrays with the third dimension representing spectral bands (e.g., M×N×KM \times N \times K for KK bands). Volumetric images, such as those from CT or MRI scans, use full 3D arrays M×N×PM \times N \times P to represent spatial depth PP, forming voxel-based volumes for three-dimensional scene reconstruction. Images are commonly stored in raster formats, which use bitmap representations consisting of uncompressed or compressed grids of values, as opposed to that define shapes via mathematical paths and equations for scalable rendering. Raster formats are preferred in image analysis for their direct pixel-level access, though they can suffer quality loss upon resizing due to fixed resolution. For lossless preservation critical to analytical workflows, file formats like TIFF (Tagged Image File Format) support high-bit-depth, multilayered storage without compression artifacts, making it suitable for professional scanning and printing applications. (Portable Network Graphics) offers efficient with transparency support, ideal for web-based or detailed graphical analysis while maintaining smaller file sizes than uncompressed bitmaps. Advanced representations enhance computational efficiency through hierarchical structures, such as image pyramids, which create multi-resolution levels by successively downsampling the original image to form a stack of lower-resolution versions. The Laplacian pyramid, for instance, encodes differences between Gaussian-smoothed levels to compactly represent details across scales, facilitating tasks like fast matching or filtering. These structures include metadata, such as () tags, which embed acquisition details like camera model, , , ISO sensitivity, timestamp, and GPS coordinates directly within the file. Color models beyond RGB, such as HSV (Hue, Saturation, Value), provide perceptually intuitive representations for analysis. The conversion from RGB to HSV involves normalizing component values to [0,1], computing the maximum (Cmax⁡C_{\max}) and minimum (Cmin⁡C_{\min}) among R', G', B', and deriving: Δ=Cmax⁡−Cmin⁡\Delta = C_{\max} - C_{\min} Hue HH is calculated as: H={0∘if Δ=060∘×(G′−B′Δmod  6)if Cmax⁡=R′60∘×(2+B′−R′Δ)if Cmax⁡=G′60∘×(4+R′−G′Δ)if Cmax⁡=B′H = \begin{cases} 0^\circ & \text{if } \Delta = 0 \\ 60^\circ \times \left( \frac{G' - B'}{\Delta} \mod 6 \right) & \text{if } C_{\max} = R' \\ 60^\circ \times \left( 2 + \frac{B' - R'}{\Delta} \right) & \text{if } C_{\max} = G' \\ 60^\circ \times \left( 4 + \frac{R' - G'}{\Delta} \right) & \text{if } C_{\max} = B' \end{cases}
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