نتایج جستجو برای: biorthogonal wavelet

تعداد نتایج: 38833  

2013
Mingwei Sheng Yongjie Pang Lei Wan Hai Huang

Autonomous underwater vehicles (AUV) are usually equipped with vision sensors. However, the underwater images captured by AUV often suffer from effects such as diffusion, scatter and caustics. So image enhancement methods are necessary to increase visual quality. A Median filter de-noising approach based on multi-wavelet transform was proposed to remove the impulse noise viewed as random noise ...

2015
Suvarna Patil Gajendra Singh Chandel Ravindra Gupta

Steganography is the art and science of writing hidden messages in such a way that no one, excluding the sender and deliberated recipient, suspects the message existence , a form of security through obscurity. Steganography techniques can be utilized to images, a video file or an audio file. Typically, steganography is written in characters including hash marking, but its usage within images is...

2003
Xiaojun Qi

Object contour matching is essential in content-based indexing and retrieval of digital images. In this paper, an automated biorthogonal-wavelet-transform-based object contour matching method is proposed. First, a wavelet transform modulus maxima (WTMM) image is produced by a one level biorthogonal wavelet transform. This WTMM image contains curvature points of the contour. Then the high curvat...

2007
Abdou Youssef

Downsampling and upsampling are widely used in image display, compression, and progressive transmission. In this paper we examine new down/upsampling methods using frequency response analysis and experimental evaluation. We consider six classes of filters for down/upsampling: decimation/duplication, bilinear interpolation, least-squares filters, orthogonal wavelets, biorthogonal wavelets, and a...

Journal: :IEEE Trans. Signal Processing 1998
Akram Aldroubi Patrice Abry Michael Unser

Starting from any two given multiresolution analyses of L2, fV 1 j gj 2Z, and fV 2 j gj 2Z, we construct biorthogonal wavelet bases that are associated with this chosen pair of multiresolutions. Thus, our construction method takes a point of view opposite to the one of Cohen–Daubechies–Feauveau (CDF), which starts from a well-chosen pair of biorthogonal discrete filters. In our construction, th...

2007
WEI-CHANG SHANN JENGNAN TZENG SHENG-WEI CHEN

We present a scheme that leverage orthonormal or biorthogonal wavelets to a new system of biorthogonal wavelets. The leveraged biorthogonal wavelets will have some nice properties. If we start with orthonormal wavelets, the leveraged scaling functions and wavelets are compactly supported and are diierentiable. The derivatives of the leveraged wavelets are orthogonal to their translations; the d...

2007
SHENG-WEI CHEN WEI-CHANG SHANN

We present a scheme that leverage orthonormal or biorthogonal wavelets to a new system of biorthogonal wavelets. The leveraged biorthogonal wavelets will have some nice properties. If we start with orthonormal wavelets, the leveraged scaling functions and wavelets are compactly supported and are diierentiable. The derivatives of the leveraged wavelets are orthogonal to their translations; the d...

2013
G. Gowri

AbstractPerformance of Orthogonal Frequency Division Multiplexing (OFDM) systems using Fast Fourier Transforms (FFT) and Discrete Wavelet Transform (DWT) are analyzed in this paper. The performance of DWT-OFDM is assessed by various parameters such as BER, eye diagram and constellation diagram. BER is the ratio of the no. of bits with error to the total no. of bits transmitted through the chann...

Journal: :CoRR 2004
Vyacheslav Zavadsky

We study image compression by a separable wavelet basis { ψ(2k1x − i)ψ(2k2y − j), φ(x − i)ψ(2k2y − j), ψ(2k1(x − i)φ(y − j), φ(x − i)φ(y − i) } , where k1, k2 ∈ Z+; i, j ∈ Z; and φ, ψ are elements of a standard biorthogonal wavelet basis in L2(R). Because k1 6= k2, the supports of the basis elements are rectangles, and the corresponding transform is known as the rectangular wavelet transform. W...

Journal: :Pattern Recognition 2010
Aditya Abhyankar Stephanie Schuckers

One important category of non-ideal conditions for iris recognition is off-angle iris images. Practically it is very difficult for images to be captured with no offset. It then becomes necessary to account for off angle information in order to maintain robust performance. A bi-orthogonal wavelet based iris recognition system, previously designed at our lab, is modified and demonstrated to perfo...

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function paginate(evt) { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term pg=parseInt(evt.target.text) var data={ "year":filter_year, "term":term, "pgn":pg } filtered_res=post_and_fetch(data,url) window.scrollTo(0,0); } function update_search_meta(search_meta) { meta_place=document.getElementById("search_meta_data") term=search_meta.term active_pgn=search_meta.pgn num_res=search_meta.num_res num_pages=search_meta.num_pages year=search_meta.year meta_place.dataset.term=term meta_place.dataset.page=active_pgn meta_place.dataset.num_res=num_res meta_place.dataset.num_pages=num_pages meta_place.dataset.year=year document.getElementById("num_result_place").innerHTML=num_res if (year !== "unfilter"){ document.getElementById("year_filter_label").style="display:inline;" document.getElementById("year_filter_place").innerHTML=year }else { document.getElementById("year_filter_label").style="display:none;" document.getElementById("year_filter_place").innerHTML="" } } function update_pagination() { search_meta_place=document.getElementById('search_meta_data') num_pages=search_meta_place.dataset.num_pages; active_pgn=parseInt(search_meta_place.dataset.page); document.getElementById("pgn-ul").innerHTML=""; pgn_html=""; for (i = 1; i <= num_pages; i++){ if (i===active_pgn){ actv="active" }else {actv=""} pgn_li="
  • " +i+ "
  • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->