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Fig. 4 – Some results obtained by the authors for a few different color samples. Reflectance curves measured using a spectrophotometer are shown as dotted lines, while the corresponding curves estimated from a real acquisition are shown as continuous lines. 33
and then ‘stitched together’ to form an image of the whole. This operation, which is called mosaicking, consists in finding corresponding details in overlapping tessels that cover adjacent areas of the scene, and then stitch those tessels together in such a way that the corresponding details perfectly overlap and the resulting composite image does not show any geometrical or color artifact, especially where the edges of the tessels were placed. Mosaicking has been extensively studied for wide-angle images like those obtained from aerial and satellite photography or panoramas taken with standard cameras (see for instance [19-21]). In these cases, tessels often show great geometrical distortions because of the lens characteristics, while the level of detail is sufficiently small, so that correcting large distortions can give good results even if some small-scale artifacts are still present. However, in many of the applications envisioned for multispectral imaging, the situation is usually different, with tessels that show very little to no geometrical distortions and are taken from slightly different view angles or slightly misaligned positions, especially on the vertical axis. On the other hand, the final mosaicked images are usually very sensitive to even small-scale artifacts. Anyway, no multispectral acquisition system can be considered complete if it does not include a framework for multispectral mosaicking. A typical approach in mosaicking is that of letting the user / operator indicate some overlapping details of two or more tessels, and then having the mosaicking software compute the corresponding mathematical transformations and produce the final mosaic. Image understanding and applied artificial intelligence techniques have also been tried to develop automated procedures that should be capable of choosing the right transformations without any intervention by the user. In these cases, however, results are mixed, as many kinds of artifacts and scenes do not lend themselves easily to this approach. Also, much research concentrated on panorama-like (i.e., horizontal) mosaicking, and the resulting algorithms perform poorly when confronted with bi-dimensional mosaicking (see Figure 5). On our part, we opted for a mixed approach and developed a semi-automatic procedure that gets input from the user under the form of corresponding areas, and then applies image analysis techniques to find the precise match to be used for computing image transformations (see Figure 6). Compared to other procedures that require single corresponding points to be indicated, this approach aims to reduce the impact of any errors caused by the limitations of the user inspection, which is performed by eye. Such errors, even when their magnitude is limited to a distance of very few pixels, may result in significantly distorted mosaicking, especially in those regions of the tessels which are distant from the detail considered. As a reference, Figure 7 shows a mosaic obtained by the authors through the application of this procedure. 34
Page 1 and 2: SOCIETÀ ITALIANA DI OTTICA E FOTON
Page 3 and 4: Proposta di una stazione a camera d
Page 5 and 6: icostruire il fattore di riflession
Page 7 and 8: costruttive. In ambito applicativo
Page 9 and 10: quindi fondamento nel salvaguardare
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Page 13 and 14: Tale ricerca di solidità è atta a
Page 15 and 16: Scanner ottico iperspettrale per la
Page 17 and 18: - R( λi ) è il fattore di rifless
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Page 21 and 22: consentendo la ripresa di immagini
Page 23 and 24: Fig. 5 - Immagine dell’interfacci
Page 25 and 26: Fig. 6 - Fattore di riflessione spe
Page 27 and 28: perpendicolare e parallela alla dir
Page 29 and 30: 3. J. Y. Hardeberg, F. Schmitt, H.
Page 31 and 32: esolution multispectral images, and
Page 33 and 34: function R. In mathematical terms,
Page 35 and 36: in standard and professional photog
Page 37: The acquisition output vectors are
Page 41 and 42: 36 Template matching Fig. 6 - A sem
Page 43 and 44: References 1. Fairchild M.D., Rosen
Page 45 and 46: 40 Sistema stereoscopico di visione
Page 47 and 48: macroscopiche sullo stato di conser
Page 49 and 50: 44 Fig. 1 - Il sistema SVA Due di e
Page 51 and 52: Si pone un piano di calibrazione, f
Page 53 and 54: a 20 000 K. I valori corrispondenti
Page 55 and 56: 50 Tabella 1 Colore LSVA xSVA ySVA
Page 57 and 58: 52 Spectral-based printer modeling
Page 59 and 60: 2. Printer Model The model of the p
Page 61 and 62: covered is computed as the differen
Page 63 and 64: The spectra were measured with a Gr
Page 65 and 66: 60 Spettrofotometro a scansione per
Page 67 and 68: icostruire il profilo tridimensiona
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Page 71 and 72: con maniglie e ruote per semplifica
Page 73 and 74: In fig. 5 sono riportati i risultat
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Page 77 and 78: inciderebbero sulla forma del fatto
Page 79 and 80: Infine in Fig. 9 sono riportati i r
Page 81 and 82: 2. D. Anglos, C. Balas, C. Fotakis,
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Per quanto riguarda la precisione d
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immagini componenti principali (aut
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6. A. Casini, F. Lotti, M. Picollo,
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� necessità di una movimentazion
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92 Trasmittanza % 90 80 70 60 50 40
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lungo l’asse di scansione. Con lo
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I valori di Riflettanza (R T % e R
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Minolta. Una matrice di corrisponde
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Tab. 3 - Confronto in termini di Δ
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Bibliografia 1. Fermi F., Antonioli
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2. Quali diagnostiche per l’Arte
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106 Fig. 3 - Rappresentazione delle
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e tre i riquadri, anche in quelli c
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Fig. 8 - PCA delle misure rilevate
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Colorimetria multispettrale per la
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stanno alla base dell’agricoltura
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116 Immagini aeree o da satellite M
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Non solo sono identificate per ogni
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vigneto, e non si sofferma certamen
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1. Introduzione 122 Spettrometria d
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Negli anni successivi, mentre veniv
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126 Fig. 5 Il software comanda poi
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Nei colori a basso contrasto, l’o
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130 Proposta di una stazione a came
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Fig. 2 − Singola immagine cattura
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134 400 500 600 700 nm lunghezza d
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6. dal confronto di misure di coppi
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Bibliografia 1. P.C. Hung, “Color
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movimento. Se infatti il filtro ste
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142 a b Fig. 2 - Trasmittanza di un
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Fig. 5 - Trasmittanza teorica di un
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dimensione della memoria di bordo p
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148 Estimating Surface Reflectance
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150 ~ ∑ j= = N w R( λ ) ( λ) (3
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R( λ) = E ⎡ 2 ⎤ ⎡ 2 ⎛ λ
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Tab. 1 - Statistics of results obta
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13. D.E. Goldberg, Genetic Algorith
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sono affetti da grandezze di influe
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160 Output(mV) 2500 2000 1500 1000
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Nel seguito del documento questa co
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appresentate nel grafico della figu
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A 166 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0
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3. Joao Carlos de Andrade, Jiulio C
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2. Generalità Richiamiamo brevemen
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spettrometro al CCD della camera. N
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Fig. 7 - Immagine fornita dalla cam
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profilo di intensità spaziale del
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Fig. 12 - Stabilità del segnale di
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9. Conclusioni Abbiamo cercato di a
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Fig. 1 - Vista parziale del MIR. Si
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pixel della matrice CCD. Purtroppo
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Determinare un riferimento dimensio
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desiderata per l’immagine finale.
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conoscono le caratteristiche spettr
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Scrovegni di Padova. Da questa anal
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