Efficient FPGA implementation of steerable Gaussian smoothers

Joginipelly, Arjun Kumar; Varela, Alvaro; Charalampidis, Dimitrios; Schott, Remy; Fitzsimmons, Zachary

doi:10.1109/ssst.2012.6195131

Cited by 27 publications

(15 citation statements)

References 8 publications

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“…The main purpose of feature extraction is to capture the important characteristics of the image (Arjun Kumar Joginipelly, 2014) (Arjun Joginipelly et al, 2012).…”

Section: (A)feature Extractionmentioning

confidence: 99%

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

et al. 2021

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Breast cancer constitutes a significant threat to women's health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes

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“…The main purpose of feature extraction is to capture the important characteristics of the image (Arjun Kumar Joginipelly, 2014) (Arjun Joginipelly et al, 2012).…”

Section: (A)feature Extractionmentioning

confidence: 99%

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

et al. 2021

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“…For large windows, several decompositions are used, for example, [21] approximates a large circularly symmetric filter by octagons. An oriented Gaussian smoother [22] was proposed for an efficient FPGA implementation. They decomposed the 2-D filter into 1-D filters and then used pipelining to obtain higher throughput.…”

Section: Proposed Rram Crossbar-based In-memory Computing Architecturementioning

confidence: 99%

RRAM Crossbar-Based In-Memory Computation of Anisotropic Filters for Image Preprocessingloa

et al. 2020

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Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering algorithm aimed at computer vision applications. The CMOS circuitry controls the scale-space image data to perform pseudo-parallel in-memory computing and nonlinear processing through RRAM crossbar. The arithmetic operations for in-memory computation of brightness gradients are efficiently accumulated to produce the enhanced image in several iterations. The proposed architecture uses single RRAM as a computing and storage element to perform both arithmetic operations and accumulations. Thanks to the in-memory computation, memory accesses and arithmetic operations are reduced by 64% and 92%, respectively, compared to traditional digital implementations. This, in turn, results in a potential reduction of power and area costs of about 75% and 85%, respectively. The processing time is also reduced by 67%.

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“…Another important characteristic of the Gaussian filter is its possibility of separation. As described in [15], separable filters enable masks of size n × n to become two masks of sizes n × 1 and 1 × n. This property divides the convolution in two operations, which allows the exploration of temporal parallelism since the second operation can start before the end of the first.…”

Section: The Gaussian Filtermentioning

confidence: 99%

Architectural Exploration of an FPGA-based Hardware Accelerator for the Gaussian Filter using Approximate Computing

Sborz

Viel

Zeferino

2020

Anais Estendidos Do X Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2020)

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The growing use of computer vision applications has increased the demand for efficient image processing implementations. These applications have constraints that, in some cases, can only be met by dedicated hardware implementations. This work presents architectures that apply approximate computing techniques to improve efficiency and scalability for implementing digital image filters on FPGA. These architectures were implemented as hardware accelerators for an embedded processor in an FPGA-based System-on-Chip. The results show that the use of approximate computing techniques can reduce costs without affecting results for the target application, which is an essential feature for further acceleration using parallel processing on hardware.

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Efficient FPGA implementation of steerable Gaussian smoothers

Cited by 27 publications

References 8 publications

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

RRAM Crossbar-Based In-Memory Computation of Anisotropic Filters for Image Preprocessingloa

Architectural Exploration of an FPGA-based Hardware Accelerator for the Gaussian Filter using Approximate Computing

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